Search results for: mode choice models

Authors: Luis Amador Jimenez, Md. Shohel Amin

Abstract:

Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

Procedia PDF Downloads 460

Authors: M. S. Mahmoud

Abstract:

This study was conducted to evaluate the manganese removal from aqueous solution using Banana peels activated carbon (BPAC). Batch experiments have been carried out to determine the influence of parameters such as pH, biosorbent dose, initial metal ion concentrations and contact times on the biosorption process. From these investigations, a significant increase in percentage removal of manganese 97.4 % is observed at pH value 5.0, biosorbent dose 0.8 g, initial concentration 20 ppm, temperature 25 ± 2 °C, stirring rate 200 rpm and contact time 2 h. The equilibrium concentration and the adsorption capacity at equilibrium of the experimental results were fitted to the Langmuir and Freundlich isotherm models; the Langmuir isotherm was found to well represent the measured adsorption data implying BPAC had heterogeneous surface. A raw groundwater samples were collected from Baharmos groundwater treatment plant network at Embaba and Manshiet Elkanater City/District-Giza, Egypt, for treatment at the best conditions that reached at first phase by BPAC. The treatment with BPAC could reduce iron and manganese value of raw groundwater by 91.4 % and 97.1 %, respectively and the effect of the treatment process on the microbiological properties of groundwater sample showed decrease of total bacterial count either at 22°C or at 37°C to 85.7 % and 82.4 %, respectively. Also, BPAC was characterized using SEM and FTIR spectroscopy.

Keywords: biosorption, banana peels, isothermal models, manganese

Procedia PDF Downloads 369

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 446

Authors: Razaq A. Olaitan, Ayansina Ayanlade

Abstract:

Understanding aerosol properties is the main goal of global research in order to lower the uncertainty associated with climate change in the trends and magnitude of aerosol particles. In order to identify aerosol particle types, optical properties, and the relationship between aerosol properties and particle concentration between 2019 and 2021, a study conducted in Ilorin, Nigeria, examined the aerosol robotic network's ground-based sun/sky scanning radiometer. The AERONET algorithm version 2 was utilized to retrieve monthly data on aerosol optical depth and angstrom exponent. The version 3 algorithm, which is an almucantar level 2 inversion, was employed to retrieve daily data on single scattering albedo and aerosol size distribution. Excel 2016 was used to analyze the data's monthly, seasonal, and annual mean averages. The distribution of different types of aerosols was analyzed using scatterplots, and the optical properties of the aerosol were investigated using pertinent mathematical theorems. To comprehend the relationships between particle concentration and properties, correlation statistics were employed. Based on the premise that aerosol characteristics must remain constant in both magnitude and trend across time and space, the study's findings indicate that the types of aerosols identified between 2019 and 2021 are as follows: 29.22% urban industrial (UI) aerosol type, 37.08% desert (D) aerosol type, 10.67% biomass burning (BB), and 23.03% urban mix (Um) aerosol type. Convective wind systems, which frequently carry particles as they blow over long distances in the atmosphere, have been responsible for the peak-of-the-columnar aerosol loadings, which were observed during August of the study period. The study has shown that while coarse mode particles dominate, fine particles are increasing in seasonal and annual trends. Burning biomass and human activities in the city are linked to these trends. The study found that the majority of particles are highly absorbing black carbon, with the fine mode having a volume median radius of 0.08 to 0.12 meters. The investigation also revealed that there is a positive coefficient of correlation (r = 0.57) between changes in aerosol particle concentration and changes in aerosol properties. Human activity is rapidly increasing in Ilorin, causing changes in aerosol properties, indicating potential health risks from climate change and human influence on geological and environmental systems.

Keywords: aerosol loading, aerosol types, health risks, optical properties

Procedia PDF Downloads 63

Authors: Madhu Jain, Rakesh Kumar Meena

Abstract:

This paper investigates a finite M/G/1 fault tolerant multi-component machining system. The system incorporates the features such as standby support, threshold recovery and imperfect coverage make the study closer to real time systems. The performance prediction of M/G/1 fault tolerant system is carried out using recursive approach by treating remaining service time as a supplementary variable. The numerical results are presented to illustrate the computational tractability of analytical results by taking three different service time distributions viz. exponential, 3-stage Erlang and deterministic. Moreover, the cost function is constructed to determine the optimal choice of system descriptors to upgrading the system.

Keywords: fault tolerant, machine repair, threshold recovery policy, imperfect coverage, supplementary variable technique

Procedia PDF Downloads 292

Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

Abstract:

Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

Procedia PDF Downloads 241

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 128

Authors: Mohammed Hasan Salahat, Stave Wade

Abstract:

This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modeling Language (UML), and an implementation pattern knows as ‘Naked Objects’. This framework have been used in action research projects that have involved the investigation and modeling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study ‘Information Retrieval System for Academic Research’ is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modeling. The framework is overviewed and justified as multi-methodology using Mingers Multi-Methodology ideas.

Keywords: SSM, UML, domain-driven design, soft domain-driven design, naked objects, soft language, information retrieval, multimethodology

Procedia PDF Downloads 560

Authors: Asim Shabir, Ruqia Shaikh

Abstract:

Supersized pricing is an effective strategy often used by marketers to make consumers buy more. However, such a strategy also results in more purchases and consumption, especially of hedonic food products. This study brings interesting insights about supersized pricing as it provides value-based justification to consumers; as a result, the guilt associated with the purchase and consumption of hedonic products diminishes, which mediates the impact between supersized pricing and size choice. Interestingly, there is a three-way interaction between pricing, product type, and health goal prime. Health prime diminishes the impact of supersized pricing in the case of more hedonic products (unhealthy) compared to less hedonic (perceived as healthy) products.

Keywords: supersized pricing, anticipated consumption guilt, health claim, product type

Procedia PDF Downloads 109

Authors: Laura Micheli, Majd Hijazi, Mahmoud Faytarouni

Abstract:

The damage reported by oil and gas industrial facilities revealed the utmost vulnerability of steel liquid storage tanks to seismic events. The failure of steel storage tanks may yield devastating and long-lasting consequences on built and natural environments, including the release of hazardous substances, uncontrolled fires, and soil contamination with hazardous materials. It is, therefore, fundamental to reliably predict the damage that steel liquid storage tanks will likely experience under future seismic hazard events. The seismic performance of steel liquid storage tanks is usually assessed using vulnerability curves obtained from the numerical simulation of a tank under different hazard scenarios. However, the computational demand of high-fidelity numerical simulation models, such as finite element models, makes the vulnerability assessment of liquid storage tanks time-consuming and often impractical. As a solution, this paper presents a surrogate model-based strategy for predicting seismic-induced damage in steel liquid storage tanks. In the proposed strategy, the surrogate model is leveraged to reduce the computational demand of time-consuming numerical simulations. To create the data set for training the surrogate model, field damage data from past earthquakes reconnaissance surveys and reports are collected. Features representative of steel liquid storage tank characteristics (e.g., diameter, height, liquid level, yielding stress) and seismic excitation parameters (e.g., peak ground acceleration, magnitude) are extracted from the field damage data. The collected data are then utilized to train a surrogate model that maps the relationship between tank characteristics, seismic hazard parameters, and seismic-induced damage via a data-driven surrogate model. Different types of surrogate algorithms, including naïve Bayes, k-nearest neighbors, decision tree, and random forest, are investigated, and results in terms of accuracy are reported. The model that yields the most accurate predictions is employed to predict future damage as a function of tank characteristics and seismic hazard intensity level. Results show that the proposed approach can be used to estimate the extent of damage in steel liquid storage tanks, where the use of data-driven surrogates represents a viable alternative to computationally expensive numerical simulation models.

Keywords: damage prediction , data-driven model, seismic performance, steel liquid storage tanks, surrogate model

Procedia PDF Downloads 143

Authors: Jawharah Alasmari

Abstract:

In inflectional morphology of verb, tense and aspect indicate action’s time either past/present or future and their period whether completed or not. The usage and meaning of tense and aspect differ in Arabic and English, therefore is no simple one -to- one mapping from an Arabic verb inflected form an appropriate English translation depends on a range of features, including immediate and wider context of use. The Quranic Arabic Corpus includes seven alternative expertly crafted English translations of each Arabic verses, which provides a test dataset for the study of appropriate Arabic to English translations of verb tense and aspect. We applied Corpus Linguistics Methods in a theoretical study of exemplary verbs, to elicit candidate verbal contexts which influence the choice of English inflection for each verse.

Keywords: Corpus linguistics methods, Arabic verb, tense and aspect, English translations

Procedia PDF Downloads 391

Authors: Fuseina Mahama, Lieselot Vanhaverbeke

Abstract:

This research investigates the impact of patronage determinants on the distance consumers are willing to travel to patronize a tire shop. Although store patronage has been acknowledged as an important domain and has received substantial research interest, most of the studies so far conducted focus on grocery retail, leaving other categories of goods widely unexplored. In this study, we focus on car tires and provide a new perspective to the specific factors that influence tire shop patronage. An online survey of consumers’ tyre purchasing behaviour was conducted among private car owners in Belgium. A sample of 864 respondents was used in the study, with almost four out of five of them being male. 84% of the respondents had purchased a car tyre in the last 24 months and on average travelled 22.4kms to patronise a tyre shop. We tested the direct and mediated effects of store choice determinants on distance consumers are willing to travel. All hypotheses were tested using Structural Equation Modelling (SEM). Our findings show that with an increase in the consumer’s age the distance they were willing to travel to a tire shop decreased. Similarly, consumers who deemed proximity an important determinant of a tire shop our findings confirmed a negative effect on willingness to travel. On the other hand, the determinants price, personal contact and professionalism all had a positive effect on distance. This means that consumers actively sought out tire shops with these characteristics and were willing to travel longer distances in order to visit them. The indirect effects of the determinants flexible opening hours, family recommendation, dealer reputation, receiving auto service at home and availability of preferred brand on distance are mediated by dealer trust. Gender had a minimal effect on distance, with females exhibiting a stronger relation in terms of dealer trust as compared to males. Overall, we found that market relevant factors were better predictors of distance; and proximity, dealer trust and professionalism have the most profound effects on distance that consumers are willing to travel. This is related to the fact that the nature of shopping goods (among which are car tires) typically reinforces consumers to be more engaged in the shopping process, therefore factors that have to do with the store (e.g. location) and shopping process play a key role in store choice decision. These findings are very specific to shopping goods and cannot be generalized to other categories of goods. For marketers and retailers these findings can have direct implications on their location strategies. The factors found to be relevant to tire shop patronage will be used in our next study to calibrate a location model to be utilised to identify the optimum location for siting new tyre shop outlets and service centres.

Keywords: dealer trust, distance to store, tire store patronage, willingness to travel

Procedia PDF Downloads 255

Authors: Obulesh Avuku, Satwik Sunnam, Sri Charan Mohan Janthuka, Keerthi Yalamaddi

Abstract:

In the United States alone, there are over 1.5 million spine fractures per year, resulting in about 17,730 spinal cord injuries. The cervical spine is where fractures in the spine most frequently occur. The prevalence of spinal fractures in the elderly has increased, and in this population, fractures may be harder to see on imaging because of coexisting degenerative illness and osteoporosis. Nowadays, computed tomography (CT) is almost completely used instead of radiography for the imaging diagnosis of adult spine fractures (x-rays). To stop neurologic degeneration and paralysis following trauma, it is vital to trace any vertebral fractures at the earliest. Many approaches have been proposed for the classification of the cervical spine [2d models]. We are here in this paper trying to break the bounds and use the vision transformers, a State-Of-The-Art- Model in image classification, by making minimal changes possible to the architecture of ViT and making it 3D-enabled architecture and this is evaluated using a weighted multi-label logarithmic loss. We have taken this problem statement from a previously held Kaggle competition, i.e., RSNA 2022 Cervical Spine Fracture Detection.

Keywords: cervical spine, spinal fractures, osteoporosis, computed tomography, 2d-models, ViT, multi-label logarithmic loss, Kaggle, public score, private score

Procedia PDF Downloads 114

Authors: Yiyuan Tao

Abstract:

Under the impact of global warming together with the intensification of human activities, the hydrological regimes may be altered, and the traditional stationary assumption was no longer satisfied. However, most of the current design standards of water infrastructures were still based on the hypothesis of stationarity, which may inevitably result in severe biases. Many critical impacts of climate on ecosystems, society, and the economy are controlled by extreme events rather than mean values. Therefore, it is of great significance to identify the non-stationarity of precipitation extremes and model the precipitation extremes in a nonstationary framework. The Wei River Basin (WRB), located in a continental monsoon climate zone in China, is selected as a case study in this study. Six extreme precipitation indices were employed to investigate the changing patterns and stationarity of precipitation extremes in the WRB. To identify if precipitation extremes are stationary, the Mann-Kendall trend test and the Pettitt test, which is used to examine the occurrence of abrupt changes are adopted in this study. Extreme precipitation indices series are fitted with non-stationary distributions that selected from six widely used distribution functions: Gumbel, lognormal, Weibull, gamma, generalized gamma and exponential distributions by means of the time-varying moments model generalized additive models for location, scale and shape (GAMLSS), where the distribution parameters are defined as a function of time. The results indicate that: (1) the trends were not significant for the whole WRB, but significant positive/negative trends were still observed in some stations, abrupt changes for consecutive wet days (CWD) mainly occurred in 1985, and the assumption of stationarity is invalid for some stations; (2) for these nonstationary extreme precipitation indices series with significant positive/negative trends, the GAMLSS models are able to capture well the temporal variations of the indices, and perform better than the stationary model. Finally, the differences between the quantiles of nonstationary and stationary models are analyzed, which highlight the importance of nonstationary modeling of precipitation extremes in the WRB.

Keywords: extreme precipitation, GAMLSSS, non-stationary, Wei River Basin

Procedia PDF Downloads 124

Authors: Mequanent Wale Mekonen, Edoardo Otranto, Angela Alibrandi

Abstract:

Diabetic kidney disease is one of the main microvascular complications caused by diabetes. Several clinical and biochemical variables are reported to be associated with diabetic kidney disease in people with type 2 diabetes. However, their interrelations could distort the effect estimation of these variables for the disease's progression. The objective of the study is to determine how the biochemical and clinical variables in people with type 2 diabetes are interrelated with each other and their effects on kidney disease progression through advanced statistical methods. First, principal component analysis was used to explore how the biochemical and clinical variables intercorrelate with each other, which helped us reduce a set of correlated biochemical variables to a smaller number of uncorrelated variables. Then, ordered logit regression models (cumulative, stage, and adjacent) were employed to assess the effect of biochemical and clinical variables on the order-level response variable (progression of kidney function) by considering the proportionality assumption for more robust effect estimation. This retrospective cross-sectional study retrieved data from a type 2 diabetic cohort in a polyclinic hospital at the University of Messina, Italy. The principal component analysis yielded three uncorrelated components. These are principal component 1, with negative loading of glycosylated haemoglobin, glycemia, and creatinine; principal component 2, with negative loading of total cholesterol and low-density lipoprotein; and principal component 3, with negative loading of high-density lipoprotein and a positive load of triglycerides. The ordered logit models (cumulative, stage, and adjacent) showed that the first component (glycosylated haemoglobin, glycemia, and creatinine) had a significant effect on the progression of kidney disease. For instance, the cumulative odds model indicated that the first principal component (linear combination of glycosylated haemoglobin, glycemia, and creatinine) had a strong and significant effect on the progression of kidney disease, with an effect or odds ratio of 0.423 (P value = 0.000). However, this effect was inconsistent across levels of kidney disease because the first principal component did not meet the proportionality assumption. To address the proportionality problem and provide robust effect estimates, alternative ordered logit models, such as the partial cumulative odds model, the partial adjacent category model, and the partial continuation ratio model, were used. These models suggested that clinical variables such as age, sex, body mass index, medication (metformin), and biochemical variables such as glycosylated haemoglobin, glycemia, and creatinine have a significant effect on the progression of kidney disease.

Keywords: diabetic kidney disease, ordered logit model, principal component analysis, type 2 diabetes

Procedia PDF Downloads 39

Authors: Saeid Sahmani

Abstract:

Due to size-dependent behavior of nanostrustures, the classical continuum models are not applicable for the analyses at this submicrion size. Surface stress effect is one of the most important matters which make the nanoscale structures to have different properties compared to the conventional structures due to high surface to volume ratio. In the present study, free vibration characteristics of nanoplates are investigated including surface stress effects. To this end, non-classical plate model based on Gurtin-Murdoch elasticity theory is proposed to evaluate the surface stress effects on the vibrational behavior of nanoplates subjected to different boundary conditions. Generalized differential quadrature (GDQ) method is employed to discretize the governing non-classical differential equations along with various edge supports. Selected numerical results are given to demonstrate the distinction between the behavior of nanoplates predicted by the classical and present non-classical plate models that leads to illustrate the great influence of surface stress effect. It is observed that this influence quite depends on the magnitude of the surface elastic constants which are relevant to the selected material.

Keywords: nanomechanics, surface stress, free vibration, GDQ method, small scale effect

Procedia PDF Downloads 356

Authors: Renan Caseiro De Almeida, Mateus Mello Garrute

Abstract:

The world walks to be ever more globalised. This trend promotes an increase on the number of transnational commercial transactions. The main modal for carriage of goods is by sea, and many countries have their economies dependent on the maritime freightage – it could be because they exercise largely this activity or because they follow the tendency of using the maritime logistic widely. Among these ones, Brazil is included. This nation counts with sixteen ports with good capacities, which receive most of the international income by sea. It is estimated that 85 per cent of the total influx of goods in Brazil is by maritime modal, leaving mere 15 per cent for the other ones. This made it necessary to develop maritime law in international and national basis, to create a standard to be applied with the intention to harmonize the transnational carriage of goods by sea. Maritime contracts are very specific and have interesting peculiarities, but in their range, little research has been made on what causes the main divergences when it comes to international contracts: the jurisdiction conflict. Likewise any other international contract, it is common for the parties to set a forum selection clause to choose the forum which will be able to judge the litigations that could rise from a maritime transport contract and, consequently, also which law should be applied to the cases. However, the forum choice in Brazil has always been somewhat polemical – not only in the maritime law sphere - for sometimes national tribunals overlook the parties’ choice and call the competence for themselves. In this sense, it is interesting to mention that the Mexico Convention of 1994 about the law applicable to international contracts did not gain strength in Brazil, nor even reached the Congress to be considered for ratification. Furthermore, it is also noteworthy that Brazil has a new Civil Procedure Code, which was put into reinforcement in 2016 bringing new legal provisions specifically about the forum selection. This represented a mark in the national legal system in this matter. Therefore, this paper intends to give an insight through Brazilian jurisprudence, making an analysis of how this issue has been treated on litigations about maritime contracts in the national tribunals, as well as the solutions found by the Brazilian legal system for the jurisdiction conflicts in those cases. To achieve the expected results, the hypothetical-deductive method will be used in combination with researches on doctrine and legislations. Also, jurisprudential research and case law study will have a special role, since the main point of this paper is to verify and study the position of the courts in Brazil in a specific matter. As a country of civil law, the Brazilian judges and tribunals are very attached to the rules displayed on codes. However, the jurisprudential understanding has been changing during the years and with the advent of the new rules about the applicable law and forum selection clause, it is noticeable that new winds are being blown.

Keywords: applicable law, forum selection clause, international business, international maritime contracts, litigation in courts

Procedia PDF Downloads 274

Authors: Mostafa Gamal, Hoda K. Mohamed, Islam El-Maddah, Ali Hamdi

Abstract:

In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs.

Keywords: graph databases, NLP, recommendation systems, similarity metrics

Procedia PDF Downloads 104

Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

Abstract:

Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

Procedia PDF Downloads 127

Authors: Khadse Saurabh, Talele Gokul, Surana Sanjay

Abstract:

In an endeavor to find a new class of anti-inflammatory agents, a series of novel benzamides (ab1-ab16) were synthesized by utilizing some arylideneoxazolones (az1-az4) having 2-acetyloxyphenyl substitution on their second position. Structures of these synthesized compounds were confirmed by IR, 1H-NMR, 13C NMR, and HRMS. Among the tested benzamide compounds 3ab1, 3ab2, 3ab11, and 3ab16 showed promising anti-inflammatory activity with lessened propensity to cause gastro-intestinal hypermotility and ulceration when compared with standard Indomethacin. Virtual screening was performed by docking the designed compounds into the ATP binding site of COX-2 receptor to predict if these compounds have analogous binding mode to the COX-2 inhibitor.

Keywords: benzamides, anti-inflammatory, gastro-intestinal hypermotility, ulcerogenic activity, docking

Procedia PDF Downloads 440

Authors: Marianne Miguet, Gaël Plantard, Yves Jaeger, Vincent Goetz

Abstract:

The contamination of groundwater is a major concern. A common pollutant, the perchloroethylene, is the target contaminant. Water treatment process as Granular Activated Carbons are very efficient but requires pilot-scale testing to determine the full-scale GAC performance. First, the batch mode was used to get a reliable experimental method to estimate the adsorption capacity of a common volatile compound is settled. The Langmuir model is acceptable to fit the isotherms. Dynamic tests were performed with three columns and different operating conditions. A database of concentration profiles and breakthroughs were obtained. The resolution of the set of differential equations is acceptable to fit the dynamics tests and could be used for a full-scale adsorber.

Keywords: activated carbon, groundwater, perchloroethylene, full-scale

Procedia PDF Downloads 426

Authors: Abd Al-Salam Al-Masgari, Haylay Tsegab, Ismailalwali Babikir, Monera A. Shoieb

Abstract:

We present a study of Malaysia's Central Luconia region, which is an essential deposit of Miocene carbonate reservoirs. This study aims to identify and map areas of selected carbonate platforms, develop high-resolution statistical karst models, and generate comprehensive karst geobody models for selected carbonate fields. This study uses seismic characterization and advanced geophysical surveys to identify karst signatures in Miocene carbonate reservoirs. The results highlight the use of variance, RMS, RGB colour blending, and 3D visualization Prop seismic sequence stratigraphy seismic attributes to visualize the karstified areas across the F23 field of Central Luconia. The offshore karst model serves as a powerful visualization tool to reveal the karstization of carbonate sediments of interest. The results of this study contribute to a better understanding of the karst distribution of Miocene carbonate reservoirs in Central Luconia, which are essential for hydrocarbon exploration and production. This is because these features significantly impact the reservoir geometry, flow path and characteristics.

Keywords: karst, central Luconia, seismic attributes, Miocene carbonate build-ups

Procedia PDF Downloads 71

Authors: Arun Prasad

Abstract:

Introduction: Laparoscopic surgery of hernia started in early 1990 and has had a mixed acceptance across the world, unlike laparoscopic cholecystectomy that has become a gold standard. Laparoscopic hernia repair claims to have less pain, less recurrence, and less wound infection compared to open hernia repair leading to early recovery and return to work. Materials and Methods: Laparoscopic hernia repair has been done in 2100 patients from 1995 till now with a follow-up data of 1350 patients. Data was analysed for results and satisfaction. Results: There is a recurrence rate of 0.1%. Early complications include bleeding, trocar injury and nerve pain. Late complications were rare. Conclusion: Laparoscopic inguinal hernia repair has a steep learning curve but after that the results and patient satisfaction are very good. It should be the procedure of choice in all bilateral and recurrent hernias.

Keywords: laparoscopy, hernia, mesh, surgery

Procedia PDF Downloads 253

Authors: Stanle Madonsela

Abstract:

For learning to take place effectively, learners have to use language. Language becomes a critical tool by which to communicate, to express feelings, desires and thoughts, and most of all to learn. However, each individual’s capacity to use language is unique. In multilingual countries, classrooms usually comprise learners from different language backgrounds, and therefore the language used for teaching and learning requires rethinking. Interaction in the classroom, if done in a language that is understood by the learners, could maximise the outcomes of learning. This paper explores the extent to which the use of a single code becomes a source of anxiety to learners in multilingual classrooms in South African schools. It contends that a multilingual approach in the learning process should be explored in order to promote learner autonomy in the learning process.

Keywords: anxiety, classroom, foreign language teaching, multilingual

Procedia PDF Downloads 536

Authors: Seyyed Ershad Moradi, Javad Khodaveisi, Atefeh Nasrollahpour

Abstract:

In this study, the highly ordered mesoporous carbon molecular sieve with high surface area and pore volume have been synthesized and modified by TiO₂ doping. The titanium oxide modified mesoporous carbon (Ti-OMC) was characterized by scanning electron microscope (SEM), BET surface area, DRS also XRD analysis (low and wide angle). Degradation experiments were conducted in batch mode with the variables such as amount of contact time, initial solution concentration, and solution pH. The optimal conditions for the degradation of methyl red (MR) were 100 mg/L dye concentration, pH of 7, and 0.12 mg/L of TiO₂ modified mesoporous carbon photocatalyst dosage.

Keywords: mesoporous carbon, photodegradation, surface modification, titanium oxide

Procedia PDF Downloads 195

Authors: Umang Varshney, Amit Karamchandani, Rohit Kapoor

Abstract:

Practitioners and researchers have realized that Blockchain is not limited to currency. Blockchain as a distributed ledger can ensure a transparent and traceable supply chain. Due to Blockchain-enabled IoTs, a firm’s information management system can now take inputs from other supply chain partners in real-time. This study aims to provide empirical evidence of dimensions responsible for blockchain implemented firm’s growth and highlight how sector (manufacturing or service), state's regulatory environment, and choice of blockchain network affect the blockchain's usefulness. This post-adoption study seeks to validate the findings of pre-adoption studies done on the blockchain. Data will be collected through a survey of managers working in blockchain implemented firms and analyzed through PLS-SEM.

Keywords: blockchain, information management system, PLS-SEM, firm's growth

Procedia PDF Downloads 126

Authors: Djeridi Rachid, Ould Ouali Mohand

Abstract:

The search of a mechanism for the elastomer abrasive wear study is an open issue. The practice difficulties are complex due to the complexity of deformation mechanism, to the complex mechanism of the material tearing and to the marked interactions between the tribological parameters. In this work, we present an experimental technique to study the elastomers abrasive wear. The interaction 'elastomer/indenter' implicate dependant ant temporary of different tribological parameters. Consequently, the phenomenon that governs this interaction is not easy to explain. An optimal elastomers compounding and an adequate utilization conditions of these materials that define its resistance at the abrasion is discussed. The results are confronted to theoretical models: the weight loss variation in function of blade angle or in function of cycle number is in agreement with rupture models and with the mechanism of fissures propagation during the material tearing in abrasive wear of filler elastomers. The weight loss in function of the sliding velocity shows the existence of a critical velocity that corresponds to the maximal wear. The adding of silica or black carbon influences in a different manner on wear abrasive behavior of filler elastomers.

Keywords: abrasion wear, filler elastomer, tribology, hyperelastic

Procedia PDF Downloads 322

Authors: Karam Ignaim, Joao M. Fernandes, Andre L. Ferreira

Abstract:

Mapping the artifacts coming from a set of similar products family developed in an ad-hoc manner to make up the resulting software product line (SPL) plays a key role to maintain the consistency between requirements and code. This paper presents a feature mapping approach that focuses on tracing the artifact coming from the migration process, the current feature model (FM), to the other artifacts of the resulting SPL, the reference architecture, and code. Thus, our approach relates each feature of the current FM to its locations in the implementation code, using the reference architecture as an intermediate artifact (as a centric point) to preserve consistency among them during an SPL evolution. The approach uses a particular artifact (i.e., traceability tree) as a solution for managing the mapping process. Tool support is provided using friendlyMapper. We have evaluated the feature mapping approach and tool support by putting the approach into practice (i.e., conducting a case study) of the automotive domain for Classical Sensor Variants Family at Bosch Car Multimedia S.A. The evaluation reveals that the mapping approach presented by this paper fits the automotive domain.

Keywords: feature location, feature models, mapping, software product lines, traceability

Procedia PDF Downloads 127

Authors: Kristina De Milt, Nicole Huang, Jihye Park

Abstract:

Opioids have been a mainstay of the treatment of chronic pain, but their overprescription and misuse have led to an opioid epidemic. Recently, as an attempt to decrease the number of opioids prescribed, the use of cannabinoid therapy has become an increasingly popular adjunctive chronic pain management choice among providers. This review of literature investigates the effects of adjunctive cannabinoids to opioids in the management of chronic pain. The nine articles are included in the literature review range from observational studies to meta-analyses published in the year 2016 and after. A majority of the studies showed a decrease in the need for opioids after adjunctive cannabinoids were introduced and, in some instances, the cessation of opioid consumption. More high-quality evidence is needed to further support this stance and providers should weigh the benefits and risks of adjunctive cannabinoids according to the clinical picture.

Keywords: cannabis, chronic pain, opioids, pain management

Procedia PDF Downloads 253

Authors: Labar Hocine, Kelaiaia Mounia Samira, Mesbah Tarek, Kelaiaia Samia

Abstract:

Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point(MPP) which depends on panels temperature and on irradiance conditions. The main drawback of P&O is that, the operating point oscillates around the MPP giving rise to the waste of some amount of available energy; moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper, it is shown that in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such initial set parameters is also carried out. The fast convergence of the proposal is proven.

Keywords: P&O, Taylor’s series, MPPT, photovoltaic panel

Procedia PDF Downloads 587