Search results for: crack growth prediction

Authors: Diqin Qi, Jiaming Li, Siman Li

Abstract:

Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

Procedia PDF Downloads 23

Authors: M. Fems Kurotimi, Agada Franklin, Godsave Aladei, Opigo Helen

Abstract:

Within the last 30 years, entrepreneurship education (EE) has continued to gain massive interest both in the field of research and among policy makers. This surge in interest can be attributed to the perceived importance EE plays in the equipping of potential entrepreneurs and as a 21^st century strategy to foster economic growth and development. This paper sets out to ascertain the correlation between EE and economic growth and development. A desk research approach was adopted where a multiplicity of literatures in the field were studied intensely. The findings reveal that indeed EE has a positive effect on entrepreneurship engagement thereby fostering economic growth and development. However, some research studies reported the contrary. That although EE may be able to equip potential entrepreneurs with requisite entrepreneurial skills and competencies, it will only be successful in producing entrepreneurs if they are internally driven to become entrepreneurs, because we cannot make people what they are not. The findings also reveal that countries that adopted EE early have more innovations inspired by entrepreneurs and are more developed than those that only recently adopted EE as a viable tool for entrepreneurship and economic development.

Keywords: entrepreneurship, entrepreneurship education, economic development, economic growth, sustainable development

Procedia PDF Downloads 324

Authors: Abdul Ganiyu Iddrisu

Abstract:

Digitization is the process of transforming analog material into digital form, especially for storage and use in a computer. Significant development of information and communication technology (ICT) over the past years has encouraged many researchers to investigate its contribution to promoting economic growth, and reducing poverty. Yet compelling empirical evidence on the effects of digitization on economic growth remains weak, particularly in Africa. This is because extant studies that explicitly evaluate digitization and economic growth nexus are mostly reports and desk reviews. This points out an empirical knowledge gap in the literature. Hypothetically, digitization influences financial sector development which in turn influences economic growth. Digitization has changed the financial sector and its operating environment. Obstacles to access to financing, for instance, physical distance, minimum balance requirements, low-income flows among others can be circumvented. Savings have increased, micro-savers have opened bank accounts, and banks are now able to price short-term loans. This has the potential to develop the financial sector, however, empirical evidence on digitization-financial development nexus is dearth. On the other hand, a number of studies maintained that financial sector development greatly influences growth of economies. We therefore argue that financial sector development is one of the transmission mechanisms through which digitization affects economic growth. Employing macro-country-level data from African countries and using fixed effects, random effects and Hausman-Taylor estimation approaches, this paper contributes to the literature by analysing economic growth in Africa focusing on the role of digitization, and financial sector development. First, we assess how digitization influence financial sector development in Africa. From an economic policy perspective, it is important to identify digitization determinants of financial sector development so that action can be taken to reduce the economic shocks associated with financial sector distortions. This nexus is rarely examined empirically in the literature. Secondly, we examine the effect of domestic credit to private sector and stock market capitalization as a percentage of GDP as used to proxy for financial sector development on 2 economic growth. Digitization is represented by the volume of digital/ICT equipment imported and GDP growth is used to proxy economic growth. Finally, we examine the effect of digitization on economic growth in the light of financial sector development. The following key results were found; first, digitalization propels financial sector development in Africa. Second, financial sector development enhances economic growth. Finally, contrary to our expectation, the results also indicate that digitalization conditioned on financial sector development tends to reduce economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improves economic growth in Africa. We, therefore, conclude that, digitalization in Africa does not only develop the financial sector but unconditionally contributes the growth of the continent’s economies.

Keywords: digitalization, economic growth, financial sector development, Africa

Procedia PDF Downloads 85

Authors: Aidan J. Bowes, Reaz Hasan

Abstract:

The automotive industry is continually aiming to develop the aerodynamics of car body design. This may be for a variety of beneficial reasons such as to increase speed or fuel efficiency by reducing drag. However recently there has been a greater amount of focus on wind noise produced while driving. Designers in this industry seek a combination of both simplicity of approach and overall effectiveness. This combined with the growing availability of commercial CFD (Computational Fluid Dynamics) packages is likely to lead to an increase in the use of RANS (Reynolds Averaged Navier-Stokes) based CFD methods. This is due to these methods often being simpler than other CFD methods, having a lower demand on time and computing power. In this investigation the effectiveness of turbulent flow and acoustic noise prediction using RANS based methods has been assessed for different wing mirror geometries. Three different RANS based models were used, standard k-ε, realizable k-ε and k-ω SST. The merits and limitations of these methods are then discussed, by comparing with both experimental and numerical results found in literature. In general, flow prediction is fairly comparable to more complex LES (Large Eddy Simulation) based methods; in particular for the k-ω SST model. However acoustic noise prediction still leaves opportunities for more improvement using RANS based methods.

Keywords: acoustics, aerodynamics, RANS models, turbulent flow

Procedia PDF Downloads 427

Authors: Parthiban Srinivasan

Abstract:

With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

Procedia PDF Downloads 339

Authors: Nishank Raisinghani

Abstract:

Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

Procedia PDF Downloads 125

Authors: Taimoor Hussain Alvi, Syed Toqueer Akhter

Abstract:

The focus of this research is to identify Pak-India bilateral trade spillover effects upon Pakistan’s Growth rate. Cross-country spillover growth Effects have been linked with openness and access to markets. In this research, we intend to see the short run and long run effects of Pak-India Bilateral Trade Openness upon economic growth in Pakistan. Trade Openness has been measured as the sum of bilateral exports and imports between the two countries. Increased emphasis on the condition and environment of financial markets is laid in light of globalization and trade liberalization. This research paper makes use of the Univariate Autoregressive Distributed Lagged Model to analyze the effects of bilateral trade variables upon the growth pattern of Pakistan in the short run and long run. Key findings of the study empirically support the notion that increased bilateral trade will be beneficial for Pakistan in the short run because of cost advantage and knowledge spillover in terms of increased technical and managerial ability from multinational firms. However, contrary to extensive literature, increased bilateral trade measures will affect Pakistan’s growth rate negatively in the long run because of the industrial size differential and increased integration of Indian economy with the world.

Keywords: bilateral trade openness, spillover, comparative advantage, univariate

Procedia PDF Downloads 464

Authors: Ling Qi, Matloob Khushi, Josiah Poon

Abstract:

This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

Procedia PDF Downloads 108

Authors: Mohammad Azam

Abstract:

This work is an endeavor to empirically investigate the Gross Domestic Product-Growth as mediating variable between various factors and portfolio returns using a broad sample of 522 financial and non-financial firms enlisted on Pakistan Stock Exchange between January-1993 and June-2022. The study employs the Structural Equation modeling and Ordinary Least Square regression to determine the findings before and during the Covid-19 epidemiological situation, which has not received due attention by researchers. The analysis reveals that market and investment factors are redundant, whereas size and value show significant results, whereas Gross Domestic Product-Growth performs significant mediating impact for the whole time frame. Using before Covid-19 period, the results reveal that market, value, and investment are redundant, but size, profitability, and Gross Domestic Product-Growth are significant. During the Covid-19, the statistics indicate that market and investment are redundant, though size and Gross Domestic Product-Growth are highly significant, but value and profitability are moderately significant. The Ordinary Least Square regression shows that market and investment are statistically insignificant, whereas size is highly significant but value and profitability are marginally significant. Using the Gross Domestic Product-Growth augmented model, a slight growth in R-square is observed. The size, value and profitability factors are recommended to the investors for Pakistan Stock Exchange. Conclusively, in the Pakistani market, the Gross Domestic Product-Growth indicates a feeble moderating effect between risk-premia and portfolio returns.

Keywords: asset pricing puzzle, mediating role of GDP-growth, structural equation modeling, COVID-19 pandemic, Pakistan stock exchange

Procedia PDF Downloads 53

Authors: T. A. Sakr, Hanaa E. Abd-El-Mottaleb

Abstract:

Outrigger-braced wall systems are commonly used to provide high rise buildings with the required lateral stiffness for wind and earthquake resistance. The existence of outriggers adds to the stiffness and strength of walls as reported by several studies. The effects of different parameters on the elasto-plastic dynamic behavior of outrigger-braced wall systems to earthquakes are investigated in this study. Parameters investigated include outrigger stiffness, concrete strength, and reinforcement arrangement as the main design parameters in wall design. In addition to being significant to the wall behavior, such parameters may lead to the change of failure mode and the delay of crack propagation and consequently failure as the wall is excited by earthquakes. Bi-linear stress-strain relation for concrete with limited tensile strength and truss members with bi-linear stress-strain relation for reinforcement were used in the finite element analysis of the problem. The famous earthquake record, El-Centro, 1940 is used in the study. Emphasis was given to the lateral drift, normal stresses and crack pattern as behavior controlling determinants. Results indicated significant effect of the studied parameters such that stiffer outrigger, higher grade concrete and concentrating the reinforcement at wall edges enhance the behavior of the system. Concrete stresses and cracking behavior are sigbificantly enhanced while lesser drift improvements are observed.

Keywords: outrigger, shear wall, earthquake, nonlinear

Procedia PDF Downloads 268

Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

Abstract:

An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

Procedia PDF Downloads 456

Authors: Tetsya Sok, Seong Jae Hong, Young Kyu Kim, Seung Woo Lee

Abstract:

The temperature distribution for asphalt concrete (AC)-Concrete composite pavement is one of main influencing factor that affects to performance life of pavement. The temperature gradient in concrete slab underneath the AC layer results the critical curling stress and lead to causes de-bonding of AC-Concrete interface. These stresses, when enhanced by repetitive axial loadings, also contribute to the fatigue damage and eventual crack development within the slab. Moreover, the temperature change within concrete slab extremely causes the slab contracts and expands that significantly induces reflective cracking in AC layer. In this paper, the numerical prediction of pavement temperature was investigated using one-dimensional finite different method (FDM) in fully explicit scheme. The numerical predicted model provides a fundamental and clear understanding of heat energy balance including incoming and outgoing thermal energies in addition to dissipated heat in the system. By using the reliable meteorological data for daily air temperature, solar radiation, wind speech and variable pavement surface properties, the predicted pavement temperature profile was validated with the field measured data. Additionally, the effects of AC thickness and daily air temperature on the temperature profile in underlying concrete were also investigated. Based on obtained results, the numerical predicted temperature of AC-Concrete composite pavement using FDM provided a good accuracy compared to field measured data and thicker AC layer significantly insulates the temperature distribution in underlying concrete slab.

Keywords: asphalt concrete, finite different method (FDM), curling effect, heat transfer, solar radiation

Procedia PDF Downloads 252

Authors: Saad M. Howladar, Mike Dennett

Abstract:

At first harvest, results showed that salinity (tap water, 100 and 200 mM NaCl) induced a significant decrease in all growth parameters in both Yecora Rojo and Paragon cultivars. The greatest effect of salinity was a decrease in leaf area. The same tendency was observed with specific leaf area, and total fresh and dry weights and their components. Green leaf and tiller numbers were reduced by the same extent in both cultivars. The corresponding final harvest, all growth parameters also reduced with increased salinity. Yield and yield components were also reduced by salinity with similar effects in both cultivars. Chlorophyll fluorescence, expressed as Fv/Fm, and gas exchange parameters were decreased significantly with increase in salinity in both cultivars. In contrast, seed protein content was increased significantly with increase in salinity. Salicylic acid (SA) application induced no significant improvements in growth parameters and yield components.

Keywords: salinity, salicylic acid, growth, chlorophyll fluorescence, gas exchange, yield

Procedia PDF Downloads 452

Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

Abstract:

This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

Procedia PDF Downloads 69

Authors: Marzieh Karimihaghighi, Carlos Castillo

Abstract:

This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

Procedia PDF Downloads 136

Authors: Abdul Ganiyu Iddrisu, Bei Chen

Abstract:

Digitization is the process of transforming analog material into digital form, especially for storage and use in a computer. Significant development of information and communication technology (ICT) over the past years has encouraged many researchers to investigate its contribution to promoting economic growth and reducing poverty. Yet the compelling empirical evidence on the effects of digitization on economic growth remains weak, particularly in Africa. This is because extant studies that explicitly evaluate digitization and economic growth nexus are mostly reports and desk reviews. This points out an empirical knowledge gap in the literature. Hypothetically, digitization influences financial sector development which in turn influences economic growth. Digitization has changed the financial sector and its operating environment. Obstacles to access to financing, for instance, physical distance, minimum balance requirements, and low-income flows, among others can be circumvented. Savings have increased, micro-savers have opened bank accounts, and banks are now able to price short-term loans. This has the potential to develop the financial sector. However, empirical evidence on the digitization-financial development nexus is dearth. On the other hand, a number of studies maintained that financial sector development greatly influences growth of economies. We, therefore, argue that financial sector development is one of the transmission mechanisms through which digitization affects economic growth. Employing macro-country-level data from African countries and using fixed effects, random effects and Hausman-Taylor estimation approaches, this paper contributes to the literature by analysing economic growth in Africa, focusing on the role of digitization and financial sector development. First, we assess how digitization influences financial sector development in Africa. From an economic policy perspective, it is important to identify digitization determinants of financial sector development so that action can be taken to reduce the economic shocks associated with financial sector distortions. This nexus is rarely examined empirically in the literature. Secondly, we examine the effect of domestic credit to the private sector and stock market capitalization as a percentage of GDP as used to proxy for financial sector development on economic growth. Digitization is represented by the volume of digital/ICT equipment imported and GDP growth is used to proxy economic growth. Finally, we examine the effect of digitization on economic growth in the light of financial sector development. The following key results were found; first, digitalization propels financial sector development in Africa. Second, financial sector development enhances economic growth. Finally, contrary to our expectation, the results also indicate that digitalization conditioned on financial sector development tends to reduce economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improve economic growth in Africa. We, therefore, conclude that, digitalization in Africa does not only develop the financial sector but unconditionally contributes the growth of the continent’s economies.

Keywords: digitalization, financial sector development, Africa, economic growth

Procedia PDF Downloads 120

Authors: Oliver Echezona Ngwu

Abstract:

The research was conducted in 2011 cropping season at the Teaching and Research farm of the Faculty of Agriculture and Natural Resources Management, Enugu State University of Science and Technology, Enugu, Nigeria to study the effect of organic and inorganic fertilizers on the growth and yield of physic Nut (Jatropha curcas). There were five treatments namely, control, (no application of treatment), NPK 20:10:10, NPK 15:15;15, poultry droppings and goat dung. The treatments were laid out in a Randomized complete Block Design (RCBD) with five replications. The total land area used was 228m2 (19x12m) while the plot size was 3mx2 (6m2). The growth parameters measured were plant height, number of leaves, and leaf area, index (LAI). The results obtained showed that there were significant differences at P=0.05 among the different treatments in 30, to and 90 DAP. Based on the results T4 (poultry droppings) had higher effect at P=0.05 at 30, 60, 90 DAP than the other treatments when compared and is hereby recommended as the best type of fertilizer for the optimum growth and production of physic Nut (Jatropha Curcas) in South Eastern Nigeria.

Keywords: organic, inorganic fertilizers, growth, yield, Jatropha curcas

Procedia PDF Downloads 264

Authors: Muhammad Ali Asadullah, Zafarullah Amir

Abstract:

In recent years, Vocational Education and Training (VET) has been under discussion by the academic researchers and remained in focus in the political grounds. Due to potential contribution of VET, the World Bank and United Nations Educational, Scientific and Cultural Organization (UNESCO) support vocational education to reduce poverty, enhance economic growth and increase competitiveness. This paper examines the impact of Vocational Education and Training on the Economic Growth and Social Inclusion with direct and mediation effect of Social Inclusion. The basic purpose of this study is to assess economic pay-offs as a result of long term investments in VET. Based on the review of Anderson Nilsson, initially we explored the increasing or decreasing trend in investment on VET. Further, the study explores that the countries which invest more on VET, tend to get more economic growth and are socially more ‘inclusive’. It is a longitudinal / panel data study with 12 years of registered data which involves 24 OECD countries. The results of the study indicate the VET has positive association with Social Inclusion and Economic Growth. Further, there is also a positive association of VET and Economic Growth through mediation of Social Inclusion. The current study considers not only issue and challenges in developing VET systems but also contributes to develop the theoretical framework for considering how VET can directly and indirectly improve economic growth and social inclusion. A wider appreciation of how VET’s benefits operate may influence a country’s decisions to invest in it. If policy makers increase investment on VET, the result would be positive in Economic Growth and Social Inclusion. It is also recommended that the same OECD model may be implemented in developing countries like Pakistan.

Keywords: Vocational Education and Training (VET), Social Inclusion, Economic Growth, OECD countries

Procedia PDF Downloads 293

Authors: Mohamed Elhakeem, A. N. Thanos Papanicolaou, Christopher Wilson, Yi-Jia Chang

Abstract:

In this study, a physically-based, modelling framework was developed to predict saturated hydraulic conductivity (KSAT) dynamics in the Clear Creek Watershed (CCW), Iowa. The modelling framework integrated selected pedotransfer functions and watershed models with geospatial tools. A number of pedotransfer functions and agricultural watershed models were examined to select the appropriate models that represent the study site conditions. Models selection was based on statistical measures of the models’ errors compared to the KSAT field measurements conducted in the CCW under different soil, climate and land use conditions. The study has shown that the predictions of the combined pedotransfer function of Rosetta and the Water Erosion Prediction Project (WEPP) provided the best agreement to the measured KSAT values in the CCW compared to the other tested models. Therefore, Rosetta and WEPP were integrated with the Geographic Information System (GIS) tools for visualization of the data in forms of geospatial maps and prediction of KSAT variability in CCW due to the seasonal changes in climate and land use activities.

Keywords: saturated hydraulic conductivity, pedotransfer functions, watershed models, geospatial tools

Procedia PDF Downloads 238

Authors: Tomas Berhanu Bekele

Abstract:

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea of avoiding traffic instabilities and homogenizing traffic flow in such a way that the risk of accidents is minimized and traffic flow is maximized. Lately, Intelligent Transport Systems (ITS) has become an important area of research to solve such road traffic-related issues for making smart decisions. It links people, roads and vehicles together using communication technologies to increase safety and mobility. Moreover, accurate prediction of road traffic is important to manage traffic congestion. The aim of this study is to develop an ANN model for the prediction of traffic flow and to compare the ANN model with the linear regression model of traffic flow predictions. Data extraction was carried out in intervals of 15 minutes from the video player. Video of mixed traffic flow was taken and then counted during office work in order to determine the traffic volume. Vehicles were classified into six categories, namely Car, Motorcycle, Minibus, mid-bus, Bus, and Truck vehicles. The average time taken by each vehicle type to travel the trap length was measured by time displayed on a video screen.

Keywords: intelligent transport system (ITS), traffic flow prediction, artificial neural network (ANN), linear regression

Procedia PDF Downloads 45

Authors: Andrey Lebedev, Ilya Dmitrenok, JooYoung Lee, Leonard Johard

Abstract:

Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm.

Keywords: graph matching, link prediction, shortest path, similarity

Procedia PDF Downloads 341

Authors: Ahmed BenSaïda

Abstract:

This paper investigates the association between financial technology (FinTech) growth and bank performance in the Gulf Cooperation Council (GCC) region. Application is conducted on a panel dataset containing the annual observations of banks covering the period from 2012 to 2021. FinTech growth is set as an explanatory variable on three proxies of bank performance. These proxies are the return on assets (ROA), return on equity (ROE), and net interest margin (NIM). Moreover, several control variables are added to the model, including bank-specific and macroeconomic variables. The results are significant as all the proxies of the bank performance are negatively affected by the growth of FinTech startups. Consequently, banks are urged to proactively invest in FinTech startups and engage in partnerships to avoid the risk of disruption.

Keywords: financial technology, bank performance, GCC countries, panel regression

Procedia PDF Downloads 62

Authors: Saad Howladar, Mike Dennett

Abstract:

At first harvest, results showed that salinity (tap water, 100 and 200 mM NaCl) induced a significant decrease in all growth parameters in both Yecora Rojo and Paragon cultivars. The greatest effect of salinity was a decrease in leaf area. The same tendency was observed with specific leaf area, and total fresh and dry weights and their components. Green leaf and tiller numbers were reduced by the same extent in both cultivars. The corresponding final harvest, all growth parameters also reduced with increased salinity. Yield and yield components were also reduced by salinity with similar effects in both cultivars. Chlorophyll fluorescence, expressed as Fv/Fm, and gas exchange parameters were decreased significantly with increase in salinity in both cultivars. In contrast, seed protein content was increased significantly with increase in salinity. Salicylic acid (SA) application induced no significant improvements in growth parameters and yield components.

Keywords: salinity, salicylic acid, growth, chlorophyll fluorescence, gas exchange, yield

Procedia PDF Downloads 402

Authors: Jaturong Matidtor, Krisna R. Torrissen, Saengtong Pongjareankit, Sudaporn Tongsiri, Jiraporn Rojtinnakorn

Abstract:

Low survival rate has being particular problem in nursery of marble goby juvenile. The aim of this study was to investigate effect of garlic extract on protein digestive pancreatic enzymes, trypsin (T) and chymotrypsin (C). The marble goby were reared with commercial feed mixed garlic extract at concentration of 0 (control), 0.3, 0.5, 1.0, 3.0 and 5.0% (w/w) for 6 weeks. Analysis of the digestive enzymes at 2 and 6 weeks was performed. Growth parameters; weight gain (WG), specific growth rate (SGR) and feed efficiency (FE), were identified. For T, C and T/C at 2 weeks, values of T and T/C ratio of 0.3% (w/w) group showed significant difference (p < 0.05) with the highest values of 17685.64± 11981.77 U/mg protein and of 51.64 ± 27.46 U/mg protein, respectively. For C at 2 weeks, 0% (w/w) group showed the highest values of 16191.76± 2225.56 U/mg protein. Whereas value of T, C and T/C ratio at 6 weeks, there was no significant difference (p > 0.05). For growth performance, it significantly increased in all garlic extract fed groups (0.3-5.0%, w/w), both at 2 and 6 weeks. At 2 weeks, values of WG and SGR of 0.5% (w/w) group showed the highest values of 71.51 ± 1.60%, and 3.85 ± 0.07%, respectively. For FE, 0.3% (w/w) group showed the highest value of 60.21 ± 6.51%. At 6 weeks, it illustrated that all growth parameters of 5.0% (w/w) group were the highest values; WG = 35.06 ± 5.66%, SGR = 2.14 ± 0.30%, and FE = 5.86 ± 0.68%. We suggested that garlic extract could be available for protein digestive enzyme and growth enhancement in marble goby nursery with artificial feed. This result will be high benefit for commercial aquaculture of marble goby.

Keywords: marble goby, nursery, garlic extract, digestive enzyme, growth

Procedia PDF Downloads 304

Authors: Esam I. Jassim

Abstract:

The main aim of this paper is to demonstrate the prediction of the model capability of predicting the nucleation process, the growth rate, and the deposition potential of second phase particles in gas flowlines. The primary objective of the research is to predict the risk hazards involved in the marine transportation of compressed natural gas. However, the proposed model can be equally used for other applications including production and transportation of natural gas in any high-pressure flow-line. The proposed model employs the following three main components to approach the problem: computational fluid dynamics (CFD) technique is used to configure the flow field; the nucleation model is developed and incorporated in the simulation to predict the incipient hydrate particles size and growth rate; and the deposition of the gas/particle flow is proposed using the concept of the particle deposition velocity. These components are integrated in a comprehended model to locate the hydrate deposition in natural gas flowlines. The present research is prepared to foresee the deposition location of solid particles that could occur in a real application in Compressed Natural Gas loading and offloading. A pipeline with 120 m length and different sizes carried a natural gas is taken in the study. The location of particle deposition formed as a result of restriction is determined based on the procedure mentioned earlier and the effect of water content and downstream pressure is studied. The critical flow speed that prevents such particle to accumulate in the certain pipe length is also addressed.

Keywords: hydrate deposition, compressed natural gas, marine transportation, oceanography

Procedia PDF Downloads 469

Authors: Neveen AlQasas, Daniel Johnson

Abstract:

Membranes for water treatment are an established technology that attracts great attention due to its simplicity and cost effectiveness. However, membranes in operation suffer from the adverse effect of membrane fouling. Bio-fouling is a phenomenon that occurs at the water-membrane interface, and is a dynamic process that is initiated by the adsorption of dissolved organic material, including biomacromolecules, on the membrane surface. After initiation, attachment of microorganisms occurs, followed by biofilm growth. The biofilm blocks the pores of the membrane and consequently results in reducing the water flux. Moreover, the presence of a fouling layer can have a substantial impact on the membrane separation properties. Understanding the mechanism of the initiation phase of biofouling is a key point in eliminating the biofouling on membrane surfaces. The adhesion and attachment of different fouling materials is affected by the surface properties of the membrane materials. Therefore, surface properties of different polymeric materials had been studied in terms of their surface energies and Hansen solubility parameters (HSP). The difference between the combined HSP parameters (HSP distance) allows prediction of the affinity of two materials to each other. The possibilities of measuring the HSP of different polymer films via surface measurements, such as contact angle has been thoroughly investigated. Knowing the HSP of a membrane material and the HSP of a specific foulant, facilitate the estimation of the HSP distance between the two, and therefore the strength of attachment to the surface. Contact angle measurements using fourteen different solvents on five different polymeric films were carried out using the sessile drop method. Solvents were ranked as good or bad solvents using different ranking method and ranking was used to calculate the HSP of each polymeric film. Results clearly indicate the absence of a direct relation between contact angle values of each film and the HSP distance between each polymer film and the solvents used. Therefore, estimating HSP via contact angle alone is not sufficient. However, it was found if the surface tensions and viscosities of the used solvents are taken in to the account in the analysis of the contact angle values, a prediction of the HSP from contact angle measurements is possible. This was carried out via training of a neural network model. The trained neural network model has three inputs, contact angle value, surface tension and viscosity of solvent used. The model is able to predict the HSP distance between the used solvent and the tested polymer (material). The HSP distance prediction is further used to estimate the total and individual HSP parameters of each tested material. The results showed an accuracy of about 90% for all the five studied films

Keywords: surface characterization, hansen solubility parameter estimation, contact angle measurements, artificial neural network model, surface measurements

Procedia PDF Downloads 75

Authors: L. R. Shobin, S. Manivannan

Abstract:

One dimensional silver nanowires and nanoparticles gained more interest in developing transparent conducting films, catalysis, biological and chemical sensors. Silver nanostructures can be synthesized by varying reaction conditions such as the precursor concentration, molar ratio of the surfactant, injection speed of silver ions, etc. in the polyol process. However, the reaction proceeds for greater than 2 hours for the formation of silver nanowires. The introduction of etchant in the medium promotes the growth of silver nanowires from silver nanoparticles along the [100] direction. Rapid growth of silver nanowires is accomplished using the Cl- ions from NaCl and polyvinyl pyrrolidone (PVP) as surfactant. The role of Cl- ion was investigated in the growth of the nanostructured silver. Silver nanoparticles (<100 nm) were harvested from glycerol medium in the absence of Cl- ions. Trace amount of Cl- ions (2.5 mM -NaCl) produced the edge joined nanowires of length upto 2 μm and width ranging from 40 to 65 nm. Formation and rapid growth (within 25 minutes) of long, uniform silver nanowires (upto 5 μm) with good yield were realized in the presence of 5 mM NaCl at 200ºC. The growth of nanostructures was monitored by UV-vis-NIR spectroscopy. Scanning and transmission electron microscopes reveal the morphology of the silver nano harvests. The role of temperature in the reduction of silver ions, growth mechanism for nanoparticles, edge joined and straight nanowires will be discussed.

Keywords: silver nanowires, glycerol mediated polyol process, scanning electron microscopy, UV-Vis- NIR spectroscopy, transmission electron microscopy

Procedia PDF Downloads 287

Authors: K. Bokreta, D. Benanaya

Abstract:

The objective of this study is to examine the relative effectiveness of monetary and fiscal policy in Algeria using the econometric modelling techniques of cointegration and vector error correction modelling to analyse and draw policy inferences. The chosen variables of fiscal policy are government expenditure and net taxes on products, while the effect of monetary policy is presented by the inflation rate and the official exchange rate. From the results, we find that in the long-run, the impact of government expenditures is positive, while the effect of taxes is negative on growth. Additionally, we find that the inflation rate is found to have little effect on GDP per capita but the impact of the exchange rate is insignificant. We conclude that fiscal policy is more powerful then monetary policy in promoting economic growth in Algeria.

Keywords: economic growth, monetary policy, fiscal policy, VECM

Procedia PDF Downloads 294

Authors: Prashant Verma

Abstract:

Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

Procedia PDF Downloads 124

Authors: Pitchayanin Sukholthaman

Abstract:

Gradually accumulated, the enormous amount of waste has caused tremendous adverse impacts to the world. Bangkok, Thailand, is chosen as an urban city of a developing country having coped with serious MSW problems due to the vast amount of waste generated, ineffective and improper waste management problems. Waste generation is the most important factor for successful planning of MSW management system. Thus, the prediction of MSW is a very important role to understand MSW distribution and characteristic; to be used for strategic planning issues. This study aims to find influencing variables that affect the amount of Bangkok MSW generation quantity.

Keywords: MSW generation, MSW quantity prediction, MSW management, multiple regression, Bangkok

Procedia PDF Downloads 405