Search results for: adsorption models
6959 Valorization of Lignocellulosic Wastes– Evaluation of Its Toxicity When Used in Adsorption Systems
Authors: Isabel Brás, Artur Figueirinha, Bruno Esteves, Luísa P. Cruz-Lopes
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The agriculture lignocellulosic by-products are receiving increased attention, namely in the search for filter materials that retain contaminants from water. These by-products, specifically almond and hazelnut shells are abundant in Portugal once almond and hazelnuts production is a local important activity. Hazelnut and almond shells have as main constituents lignin, cellulose and hemicelluloses, water soluble extractives and tannins. Along the adsorption of heavy metals from contaminated waters, water soluble compounds can leach from shells and have a negative impact in the environment. Usually, the chemical characterization of treated water by itself may not show environmental impact caused by the discharges when parameters obey to legal quality standards for water. Only biological systems can detect the toxic effects of the water constituents. Therefore, the evaluation of toxicity by biological tests is very important when deciding the suitability for safe water discharge or for irrigation applications. The main purpose of the present work was to assess the potential impacts of waters after been treated for heavy metal removal by hazelnut and almond shells adsorption systems, with short term acute toxicity tests. To conduct the study, water at pH 6 with 25 mg.L-1 of lead, was treated with 10 g of shell per litre of wastewater, for 24 hours. This procedure was followed for each bark. Afterwards the water was collected for toxicological assays; namely bacterial resistance, seed germination, Lemna minor L. test and plant grow. The effect in isolated bacteria strains was determined by disc diffusion method and the germination index of seed was evaluated using lettuce, with temperature and humidity germination control for 7 days. For aquatic higher organism, Lemnas were used with 4 days contact time with shell solutions, in controlled light and temperature. For terrestrial higher plants, biomass production was evaluated after 14 days of tomato germination had occurred in soil, with controlled humidity, light and temperature. Toxicity tests of water treated with shells revealed in some extent effects in the tested organisms, with the test assays showing a close behaviour as the control, leading to the conclusion that its further utilization may not be considered to create a serious risk to the environment.Keywords: lignocellulosic wastes, adsorption, acute toxicity tests, risk assessment
Procedia PDF Downloads 3646958 Evaluation of Football Forecasting Models: 2021 Brazilian Championship Case Study
Authors: Flavio Cordeiro Fontanella, Asla Medeiros e Sá, Moacyr Alvim Horta Barbosa da Silva
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In the present work, we analyse the performance of football results forecasting models. In order to do so, we have performed the data collection from eight different forecasting models during the 2021 Brazilian football season. First, we guide the analysis through visual representations of the data, designed to highlight the most prominent features and enhance the interpretation of differences and similarities between the models. We propose using a 2-simplex triangle to investigate visual patterns from the results forecasting models. Next, we compute the expected points for every team playing in the championship and compare them to the final league standings, revealing interesting contrasts between actual to expected performances. Then, we evaluate forecasts’ accuracy using the Ranked Probability Score (RPS); models comparison accounts for tiny scale differences that may become consistent in time. Finally, we observe that the Wisdom of Crowds principle can be appropriately applied in the context, driving into a discussion of results forecasts usage in practice. This paper’s primary goal is to encourage football forecasts’ performance discussion. We hope to accomplish it by presenting appropriate criteria and easy-to-understand visual representations that can point out the relevant factors of the subject.Keywords: accuracy evaluation, Brazilian championship, football results forecasts, forecasting models, visual analysis
Procedia PDF Downloads 956957 Poly(S/DVB)HIPE Filled with Cellulose from Water Hyacinth
Authors: Metinee Kawsomboon, Thanchanok Tulaphol, Manit Nithitanakul, Jitima Preechawong
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PolyHIPE is a porous polymeric material from polymerization of high internal phase emulsion (HIPE) which contains 74% of internal phase (disperse phase) and 26 % of external phase (continues phase). Typically, polyHIPE was prepared from styrene (S) and divinylbenzene (DVB) and they were used in various kind of applications such as catalyst support, gas adsorption, separation membranes, and tissue engineering scaffolds due to high specific surface areas, high porousity, ability to adsorb large quantities of liquid. In this research, cellulose from water hyacinth (Eichornia Crassipes), an aquatic plant that grows and spread rapidly in rivers and waterways in Thailand was added into polyHIPE to increase mechanical property of polyHIPE. Addition of unmodified and modified cellulose to poly(S/DVB)HIPE resulting in a decrease in the surface area and thermal stability of the resulting materials. Mechanical properties of the resulting polyHIPEs filled with both unmodified and modified cellulose exhibited higher compressive strength and Young’s modulus by 146.3% and 162.5% respectively, compared to unfilled polyHIPEs. The water adsorption capacity of filled polyHIPE was also improved.Keywords: porous polymer, PolyHIPE, cellulose, surface modification, water hyacinth
Procedia PDF Downloads 1406956 Statistical Channel Modeling for Multiple-Input-Multiple-Output Communication System
Authors: M. I. Youssef, A. E. Emam, M. Abd Elghany
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The performance of wireless communication systems is affected mainly by the environment of its associated channel, which is characterized by dynamic and unpredictable behavior. In this paper, different statistical earth-satellite channel models are studied with emphasize on two main models, first is the Rice-Log normal model, due to its representation for the environment including shadowing and multi-path components that affect the propagated signal along its path, and a three-state model that take into account different fading conditions (clear area, moderate shadow and heavy shadowing). The provided models are based on AWGN, Rician, Rayleigh, and log-normal distributions were their Probability Density Functions (PDFs) are presented. The transmission system Bit Error Rate (BER), Peak-Average-Power Ratio (PAPR), and the channel capacity vs. fading models are measured and analyzed. These simulations are implemented using MATLAB tool, and the results had shown the performance of transmission system over different channel models.Keywords: fading channels, MIMO communication, RNS scheme, statistical modeling
Procedia PDF Downloads 1466955 Managing Diversity in MNCS: A Literature Review of Existing Strategic Models for Managing Diversity and a Roadmap to Transfer Them to the Subsidiaries
Authors: Debora Gottardello, Mireia Valverde Aparicio, Juan Llopis Taverner
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Globalization has given rise to a great diversity in the composition of people in organizations. Diversity management is therefore key to create growth in today’s competitive global marketplace. This work develops a literature review related to the existing models for managing diversity covering the period from 1980 until 2014. Furthermore, it identifies limitations in previous models. More specifically, the literature review reveals that there is a lack of information about how these models can be adapted from the headquarters to the subsidiaries. Therefore, the contribution of this paper is to suggest how the models should be adapted when they are directed to host countries. Our aim is to highlight the limitations of the developed models with regards to the translation of the diversity management practices to the subsidiaries. Accordingly, a model that will enable MNCs to ensure a global strategy is suggested. Taking advantage of the potential incorporated in a culturally diverse work team should be at the top of every international company’s aims. Executives from headquarters need to use different attitudes when transferring diversity practices towards their subsidiaries. Further studies should reassess local practices of diversity management to find out how this universal management model is translated.Keywords: culture diversity, diversity management, human resources management, MNCs, subsidiaries, workforce diversity
Procedia PDF Downloads 2536954 Photocatalytic Degradation of Lead from Aqueous Solution Using TiO2 as Adsorbent
Authors: Navven Desai, Veena Soraganvi
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Heavy metals such as lead, cadmium and mercury do not have biological significance hence they are known to be extremely toxic heavy metals. Water contains various heavy metals like Cadmium (Cd), Chromium (Cr), Copper (Cu), Nickel (Ni), Arsenic (As), Lead (Pb), and Zinc (Zn) etc., when it gets polluted with industrial waste water. These heavy metals cause various health effects even at low concentration when consumed by humans. Most of the heavy metals are poisonous to living organisms. Heavy metals are non-degradable and are preserved in the environment through bioaccumulation. Therefore removal of heavy metals from water is necessary. In recent years, a great deal of attentions has been focused on to the application of nanosized metal oxides to treat heavy metals, especially titanium oxides, ferric oxides, manganese oxides, aluminium oxides and magnesium oxides as adsorbent and photocatalyst. TiO2 based photocatalysts have attracted continuously increasing attention because of the excellent properties such as high light -conversion efficiency, chemical stability, nontoxic nature, low cost. The catalyst displays high photocatalytic activity because of its large surface area. In this study, the photocatalytic degradation of Lead (Pb) from aqueous solution was investigated in natural sunlight by using TiO2 as Nanomaterial. This study was performed at laboratory scale. All the experiments were carried out in the batch process. The concentration of lead was constant (25mg/lit) in the experiment and effect of titanium dioxide dose and pH were varied to study the removal efficiency of the lead by adsorption. Further study was performed on the dependence of photocatalytic reaction on the reaction temperature. The aqueous solution was prepared by Lead metal powder. TiO2 photo catalyst nanopowder used was Sisco-74629 grade. The heavy metal is analyzed with VARIAN AA 240 atomic adsorption spectrophotometer. The study shows, with increasing TiO2 dose and pH the lead removal increases. According to study, it can be concluded that the utilization of titanium dioxide accounted for higher efficiency in the removal of lead from aqueous solution.Keywords: adsorption, heavy metals, nanomaterial, photocatalysis
Procedia PDF Downloads 2976953 Numerical Investigation of the Effect of Blast Pressure on Discrete Model in Shock Tube
Authors: Aldin Justin Sundararaj, Austin Lord Tennyson, Divya Jose, A. N. Subash
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Blast waves are generated due to the explosions of high energy materials. An explosion yielding a blast wave has the potential to cause severe damage to buildings and its personnel. In order to understand the physics of effects of blast pressure on buildings, studies in the shock tube on generic configurations are carried out at various pressures on discrete models. The strength of shock wave is systematically varied by using different driver gases and diaphragm thickness. The basic material of the diaphragm is Aluminum. To simulate the effect of shock waves on discrete models a shock tube was used. Generic models selected for this study are suitably scaled cylinder, cone and cubical blocks. The experiments were carried out with 2mm diaphragm with burst pressure ranging from 28 to 31 bar. Numerical analysis was carried out over these discrete models. A 3D model of shock-tube with different discrete models inside the tube was used for CFD computation. It was found that cone has dissipated most of the shock pressure compared to cylinder and cubical block. The robustness and the accuracy of the numerical model were validation with the analytical and experimental data.Keywords: shock wave, blast wave, discrete models, shock tube
Procedia PDF Downloads 3286952 Poly (L-Lysine)-Coated Liquid Crystal Droplets for Sensitive Detection of DNA and Its Applications in Controlled Release of Drug Molecules
Authors: Indu Verma, Santanu Kumar Pal
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Interactions between DNA and adsorbed Poly (L-lysine) (PLL) on liquid crystal (LC) droplets were investigated using polarizing optical microcopy (POM) and epi-fluorescence microscopy. Earlier, we demonstrated that adsorption of PLL to the LC/aqueous interface resulted in homeotropic orientation of the LC and thus exhibited a radial configuration of the LC confined within the droplets. Subsequent adsorption of DNA (single stranded DNA/double stranded DNA) at PLL coated LC droplets was found to trigger a LC reorientation within the droplets leading to pre-radial/bipolar configuration of those droplets. To our surprise, subsequent exposure of complementary ssDNA (c-ssDNA) to ssDNA/ adsorbed PLL modified LC droplets did not cause the LC reorientation. This is likely due to the formation of polyplexes (DNA-PLL complex) as confirmed by fluorescence microscopy and atomic force microscopy. In addition, dsDNA adsorbed PLL droplets have been found to be effectively used to displace (controlled release) propidium iodide (a model drug) encapsulated within dsDNA over time. These observations suggest the potential for a label free droplet based LC detection system that can respond to DNA and may provide a simple method to develop DNA-based drug nano-carriers.Keywords: DNA biosensor, drug delivery, interfaces, liquid crystal droplets
Procedia PDF Downloads 2976951 Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network
Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu
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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.Keywords: big data, k-NN, machine learning, traffic speed prediction
Procedia PDF Downloads 3626950 A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata
Authors: Pavan K. Rallabandi, Kailash C. Patidar
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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata
Procedia PDF Downloads 3876949 Bimetallic MOFs Based Membrane for the Removal of Heavy Metal Ions from the Industrial Wastewater
Authors: Muhammad Umar Mushtaq, Muhammad Bilal Khan Niazi, Nouman Ahmad, Dooa Arif
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Apart from organic dyes, heavy metals such as Pb, Ni, Cr, and Cu are present in textile effluent and pose a threat to humans and the environment. Many studies on removing heavy metallic ions from textile wastewater have been conducted in recent decades using metal-organic frameworks (MOFs). In this study new polyether sulfone ultrafiltration membrane, modified with Cu/Co and Cu/Zn-based bimetal-organic frameworks (MOFs), was produced. Phase inversion was used to produce the membrane, and atomic force microscopy (AFM), scanning electron microscopy (SEM) were used to characterize it. The bimetallic MOFs-based membrane structure is complex and can be comprehended using characterization techniques. The bimetallic MOF-based filtration membranes are designed to selectively adsorb specific contaminants while allowing the passage of water molecules, improving the ultrafiltration efficiency. MOFs' adsorption capacity and selectivity are enhanced by functionalizing them with particular chemical groups or incorporating them into composite membranes with other materials, such as polymers. The morphology and performance of the bimetallic MOF-based membrane were investigated regarding pure water flux and metal ion rejection. The advantages of developed bimetallic MOFs based membranes for wastewater treatment include enhanced adsorption capacity because of the presence of two metals in their structure, which provides additional binding sites for contaminants, leading to a higher adsorption capacity and more efficient removal of pollutants from wastewater. Based on the experimental findings, bimetallic MOF-based membranes are more capable of rejecting metal ions from industrial wastewater than conventional membranes that have already been developed. Furthermore, the difficulties associated with operational parameters, including pressure gradients and velocity profiles, are simulated using Ansys Fluent software. The simulation results obtained for the operating parameters are in complete agreement with the experimental results.Keywords: bimetallic MOFs, heavy metal ions, industrial wastewater treatment, ultrafiltration.
Procedia PDF Downloads 886948 Leverage Effect for Volatility with Generalized Laplace Error
Authors: Farrukh Javed, Krzysztof Podgórski
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We propose a new model that accounts for the asymmetric response of volatility to positive ('good news') and negative ('bad news') shocks in economic time series the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape, that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional distribution of 'bad' and 'good' news processes given the past the property that is important for the statistical fitting of the model. Relevant features of volatility models are illustrated using S&P 500 historical data.Keywords: heavy tails, volatility clustering, generalized asymmetric laplace distribution, leverage effect, conditional heteroskedasticity, asymmetric power volatility, GARCH models
Procedia PDF Downloads 3836947 Adsorption and Corrosion Inhibition of New Synthesized Thiophene Schiff Base on Mild Steel in HCL Solution
Authors: H. Elmsellem, A. Aouniti, S. Radi, A. Chetouani, B. Hammouti
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The synthesis of new organic molecules offers various molecular structures containing heteroatoms and substituents for corrosion protection in acid pickling of metals. The most synthesized compounds are the nitrogen heterocyclic compounds, which are known to be excellent complex or chelate forming substances with metals. The choice of the inhibitor is based on two considerations: first it could be synthesized conveniently from relatively cheap raw materials, secondly, it contains the electron cloud on the aromatic ring or, the electro negative atoms such as nitrogen and oxygen in the relatively long chain compounds. In the present study, (NE)‐2‐methyl‐N‐(thiophen‐2‐ylmethylidene) aniline(T) was synthesized and its inhibiting action on the corrosion of mild steel in 1 M hydrochloric acid was examined by different corrosion methods, such as weight loss, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The experimental results suggest that this compound is an efficient corrosion inhibitor and the inhibition efficiency increases with the increase in inhibitor concentration. Adsorption of this compound on mild steel surface obeys Langmuir’s isotherm. Correlation between quantum chemical calculations and inhibition efficiency of the investigated compound is discussed using the Density Functional Theory method (DFT).Keywords: mild steel, Schiff base, inhibition, corrosion, HCl, quantum chemical
Procedia PDF Downloads 3316946 Analyzing Business Model Choices and Sustainable Value Capturing: A Multiple Case Study of Sharing Economy Business Models
Authors: Minttu Laukkanen, Janne Huiskonen
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This study investigates the sharing economy business models as examples of the sustainable business models. The aim is to contribute to the limited literature on sharing economy in connection with sustainable business models by explaining sharing economy business models value capturing. Specifically, this research answers the following question: How business model choices affect captured sustainable value? A multiple case study approach is applied in this study. Twenty different successful sharing economy business models focusing on consumer business and covering four main areas, accommodation, mobility, food, and consumer goods, are selected for analysis. The secondary data available on companies’ websites, previous research, reports, and other public documents are used. All twenty cases are analyzed through the sharing economy business model framework and sustainable value analysis framework using qualitative data analysis. This study represents general sharing economy business model value attributes and their specifications, i.e. sustainable value propositions for different stakeholders, and further explains the sustainability impacts of different sharing economy business models through captured and uncaptured value. In conclusion, this study represents how business model choices affect sustainable value capturing through eight business model attributes identified in this study. This paper contributes to the research on sustainable business models and sharing economy by examining how business model choices affect captured sustainable value. This study highlights the importance of careful business model and sustainability impacts analyses including the triple bottom line, multiple stakeholders and value captured and uncaptured perspectives as well as sustainability trade-offs. It is not self-evident that sharing economy business models advance sustainability, and business model choices does matter.Keywords: sharing economy, sustainable business model innovation, sustainable value, value capturing
Procedia PDF Downloads 1716945 Generic Hybrid Models for Two-Dimensional Ultrasonic Guided Wave Problems
Authors: Manoj Reghu, Prabhu Rajagopal, C. V. Krishnamurthy, Krishnan Balasubramaniam
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A thorough understanding of guided ultrasonic wave behavior in structures is essential for the application of existing Non Destructive Evaluation (NDE) technologies, as well as for the development of new methods. However, the analysis of guided wave phenomena is challenging because of their complex dispersive and multimodal nature. Although numerical solution procedures have proven to be very useful in this regard, the increasing complexity of features and defects to be considered, as well as the desire to improve the accuracy of inspection often imposes a large computational cost. Hybrid models that combine numerical solutions for wave scattering with faster alternative methods for wave propagation have long been considered as a solution to this problem. However usually such models require modification of the base code of the solution procedure. Here we aim to develop Generic Hybrid models that can be directly applied to any two different solution procedures. With this goal in mind, a Numerical Hybrid model and an Analytical-Numerical Hybrid model has been developed. The concept and implementation of these Hybrid models are discussed in this paper.Keywords: guided ultrasonic waves, Finite Element Method (FEM), Hybrid model
Procedia PDF Downloads 4636944 Computational Models for Accurate Estimation of Joint Forces
Authors: Ibrahim Elnour Abdelrahman Eltayeb
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Computational modelling is a method used to investigate joint forces during a movement. It can get high accuracy in the joint forces via subject-specific models. However, the construction of subject-specific models remains time-consuming and expensive. The purpose of this paper was to identify what alterations we can make to generic computational models to get a better estimation of the joint forces. It appraised the impact of these alterations on the accuracy of the estimated joint forces. It found different strategies of alterations: joint model, muscle model, and an optimisation problem. All these alterations affected joint contact force accuracy, so showing the potential for improving the model predictions without involving costly and time-consuming medical images.Keywords: joint force, joint model, optimisation problem, validation
Procedia PDF Downloads 1686943 Modelling Mode Choice Behaviour Using Cloud Theory
Authors: Leah Wright, Trevor Townsend
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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty
Procedia PDF Downloads 3866942 Simulation of Channel Models for Device-to-Device Application of 5G Urban Microcell Scenario
Authors: H. Zormati, J. Chebil, J. Bel Hadj Tahar
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Next generation wireless transmission technology (5G) is expected to support the development of channel models for higher frequency bands, so clarification of high frequency bands is the most important issue in radio propagation research for 5G, multiple urban microcellular measurements have been carried out at 60 GHz. In this paper, the collected data is uniformly analyzed with focus on the path loss (PL), the objective is to compare simulation results of some studied channel models with the purpose of testing the performance of each one.Keywords: 5G, channel model, 60GHz channel, millimeter-wave, urban microcell
Procedia PDF Downloads 3186941 Advancing Environmental Remediation Through the Production of Functional Porous Materials from Phosphorite Residue Tailings
Authors: Ali Mohammed Yimer, Ayalew Assen, Youssef Belmabkhout
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Environmental remediation is a pressing global concern, necessitating innovative strategies to address the challenges posed by industrial waste and pollution. This study aims to advance environmental remediation by developing cutting-edge functional porous materials from phosphorite residue tailings. Phosphorite mining activities generate vast amounts of waste, which pose significant environmental risks due to their contaminants. The proposed approach involved transforming these phosphorite residue tailings into valuable porous materials through a series of physico-chemical processes including milling, acid-base leaching, designing or templating as well as formation processes. The key components of the tailings were extracted and processed to produce porous arrays with high surface area and porosity. These materials were engineered to possess specific properties suitable for environmental remediation applications, such as enhanced adsorption capacity and selectivity for target contaminants. The synthesized porous materials were thoroughly characterized using advanced analytical techniques (XRD, SEM-EDX, N2 sorption, TGA, FTIR) to assess their structural, morphological, and chemical properties. The performance of the materials in removing various pollutants, including heavy metals and organic compounds, were evaluated through batch adsorption experiments. Additionally, the potential for material regeneration and reusability was investigated to enhance the sustainability of the proposed remediation approach. The outdoors of this research holds significant promise for addressing the environmental challenges associated with phosphorite residue tailings. By valorizing these waste materials into porous materials with exceptional remediation capabilities, this study contributes to the development of sustainable and cost-effective solutions for environmental cleanup. Furthermore, the utilization of phosphorite residue tailings in this manner offers a potential avenue for the remediation of other contaminated sites, thereby fostering a circular economy approach to waste management.Keywords: functional porous materials, phosphorite residue tailings, adsorption, environmental remediation, sustainable solutions
Procedia PDF Downloads 576940 Contrasted Mean and Median Models in Egyptian Stock Markets
Authors: Mai A. Ibrahim, Mohammed El-Beltagy, Motaz Khorshid
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Emerging Markets return distributions have shown significance departure from normality were they are characterized by fatter tails relative to the normal distribution and exhibit levels of skewness and kurtosis that constitute a significant departure from normality. Therefore, the classical Markowitz Mean-Variance is not applicable for emerging markets since it assumes normally-distributed returns (with zero skewness and kurtosis) and a quadratic utility function. Moreover, the Markowitz mean-variance analysis can be used in cases of moderate non-normality and it still provides a good approximation of the expected utility, but it may be ineffective under large departure from normality. Higher moments models and median models have been suggested in the literature for asset allocation in this case. Higher moments models have been introduced to account for the insufficiency of the description of a portfolio by only its first two moments while the median model has been introduced as a robust statistic which is less affected by outliers than the mean. Tail risk measures such as Value-at Risk (VaR) and Conditional Value-at-Risk (CVaR) have been introduced instead of Variance to capture the effect of risk. In this research, higher moment models including the Mean-Variance-Skewness (MVS) and Mean-Variance-Skewness-Kurtosis (MVSK) are formulated as single-objective non-linear programming problems (NLP) and median models including the Median-Value at Risk (MedVaR) and Median-Mean Absolute Deviation (MedMAD) are formulated as a single-objective mixed-integer linear programming (MILP) problems. The higher moment models and median models are compared to some benchmark portfolios and tested on real financial data in the Egyptian main Index EGX30. The results show that all the median models outperform the higher moment models were they provide higher final wealth for the investor over the entire period of study. In addition, the results have confirmed the inapplicability of the classical Markowitz Mean-Variance to the Egyptian stock market as it resulted in very low realized profits.Keywords: Egyptian stock exchange, emerging markets, higher moment models, median models, mixed-integer linear programming, non-linear programming
Procedia PDF Downloads 3136939 The Influence of Covariance Hankel Matrix Dimension on Algorithms for VARMA Models
Authors: Celina Pestano-Gabino, Concepcion Gonzalez-Concepcion, M. Candelaria Gil-Fariña
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Some estimation methods for VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. It is known that if the data sample is populous enough and the dimension of the Hankel matrix is unnecessarily large, this may result in an unnecessary number of computations as well as in numerical problems. In this sense, the aim of this paper is two-fold. First, we provide some theoretical results for these matrices which translate into a lower dimension for the matrices normally used in the algorithms. This contribution thus serves to improve those methods from a numerical and, presumably, statistical point of view. Second, we have chosen an estimation algorithm to illustrate in practice our improvements. The results we obtained in a simulation of VARMA models show that an increase in the size of the Hankel matrix beyond the theoretical bound proposed as valid does not necessarily lead to improved practical results. Therefore, for future research, we propose conducting similar studies using any of the linear system estimation methods that depend on Hankel matrices.Keywords: covariances Hankel matrices, Kronecker indices, system identification, VARMA models
Procedia PDF Downloads 2416938 Investigation on the Capacitive Deionization of Functionalized Carbon Nanotubes (F-CNTs) and Silver-Decorated F-CNTs for Water Softening
Authors: Khrizelle Angelique Sablan, Rizalinda De Leon, Jaeyoung Lee, Joey Ocon
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The impending water shortage drives us to find alternative sources of water. One of the possible solutions is desalination of seawater. There are numerous processes by which it can be done and one if which is capacitive deionization. Capacitive deionization is a relatively new technique for water desalination. It utilizes the electric double layer for ion adsorption. Carbon-based materials are commonly used as electrodes for capacitive deionization. In this study, carbon nanotubes (CNTs) were treated in a mixture of nitric and sulfuric acid. The silver addition was also facilitated to incorporate antimicrobial action. The acid-treated carbon nanotubes (f-CNTs) and silver-decorated f-CNTs (Ag@f-CNTs) were used as electrode materials for seawater deionization and compared with CNT and acid-treated CNT. The synthesized materials were characterized using TEM, EDS, XRD, XPS and BET. The electrochemical performance was evaluated using cyclic voltammetry, and the deionization performance was tested on a single cell with water containing 64mg/L NaCl. The results showed that the synthesized Ag@f-CNT-10 H could have better performance than CNT and a-CNT with a maximum ion removal efficiency of 50.22% and a corresponding adsorption capacity of 3.21 mg/g. It also showed antimicrobial activity against E. coli. However, the said material lacks stability as the efficiency decreases with repeated usage of the electrode.Keywords: capacitive deionization, carbon nanotubes, desalination, acid functionalization, silver
Procedia PDF Downloads 2296937 Energy Consumption Models for Electric Vehicles: Survey and Proposal of a More Realistic Model
Authors: I. Sagaama, A. Kechiche, W. Trojet, F. Kamoun
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Replacing combustion engine vehicles by electric vehicles (EVs) is a major step in recent years due to their potential benefits. Battery autonomy and charging processes are still a big issue for that kind of vehicles. Therefore, reducing the energy consumption of electric vehicles becomes a necessity. Many researches target introducing recent information and communication technologies in EVs in order to propose reducing energy consumption services. Evaluation of realistic scenarios is a big challenge nowadays. In this paper, we will elaborate a state of the art of different proposed energy consumption models in the literature, then we will present a comparative study of these models, finally, we will extend previous works in order to propose an accurate and realistic energy model for calculating instantaneous power consumption of electric vehicles.Keywords: electric vehicle, vehicular networks, energy models, traffic simulation
Procedia PDF Downloads 3686936 Generation of 3d Models Obtained with Low-Cost RGB and Thermal Sensors Mounted on Drones
Authors: Julio Manuel De Luis Ruiz, Javier Sedano Cibrián, RubéN Pérez Álvarez, Raúl Pereda García, Felipe Piña García
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Nowadays it is common to resort to aerial photography to carry out the prospection and/or exploration of archaeological sites. In this sense, the classic 3D models are being applied to investigate the direction towards which the generally subterranean structures of an archaeological site may continue and therefore, to help in making the decisions that define the location of new excavations. In recent years, Unmanned Aerial Vehicles (UAVs) have been applied as the vehicles that carry the sensor. This implies certain advantages, such as the possibility of including low-cost sensors, given that these vehicles can carry the sensor at relatively low altitudes. Due to this, low-cost dual sensors have recently begun to be used. This new equipment can collaborate with classic Digital Elevation Models (DEMs) in the exploration of archaeological sites, but this entails the need for a methodological setting to optimise the acquisition, processing and exploitation of the information provided by low-cost dual sensors. This research focuses on the design of an appropriate workflow to obtain 3D models with low-cost sensors carried on UAVs, both in the RGB and thermal domains. All the foregoing has been applied to the archaeological site of Juliobriga, located in Cantabria (Spain).Keywords: process optimization, RGB models, thermal models, , UAV, workflow
Procedia PDF Downloads 1376935 Recognition of Cursive Arabic Handwritten Text Using Embedded Training Based on Hidden Markov Models (HMMs)
Authors: Rabi Mouhcine, Amrouch Mustapha, Mahani Zouhir, Mammass Driss
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In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.Keywords: recognition, handwriting, Arabic text, HMMs, embedded training
Procedia PDF Downloads 3526934 Stochastic Age-Structured Population Models
Authors: Arcady Ponosov
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Many well-known age-structured population models are derived from the celebrated McKendrick-von Foerster equation (MFE), also called the biological conservation law. A similar technique is suggested for the stochastically perturbed MFE. This technique is shown to produce stochastic versions of the deterministic population models, which appear to be very different from those one can construct by simply appending additive stochasticity to deterministic equations. In particular, it is shown that stochastic Nicholson’s blowflies model should contain both additive and multiplicative stochastic noises. The suggested transformation technique is similar to that used in the deterministic case. The difference is hidden in the formulas for the exact solutions of the simplified boundary value problem for the stochastically perturbed MFE. The analysis is also based on the theory of stochastic delay differential equations.Keywords: boundary value problems, population models, stochastic delay differential equations, stochastic partial differential equation
Procedia PDF Downloads 2536933 A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction
Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju
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The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.Keywords: comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events
Procedia PDF Downloads 2606932 From Problem Space to Executional Architecture: The Development of a Simulator to Examine the Effect of Autonomy on Mainline Rail Capacity
Authors: Emily J. Morey, Kevin Galvin, Thomas Riley, R. Eddie Wilson
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The key challenges faced by integrating autonomous rail operations into the existing mainline railway environment have been identified through the understanding and framing of the problem space and stakeholder analysis. This was achieved through the completion of the first four steps of Soft Systems Methodology, where the problem space has been expressed via conceptual models. Having identified these challenges, we investigated one of them, namely capacity, via the use of models and simulation. This paper examines the approach used to move from the conceptual models to a simulation which can determine whether the integration of autonomous trains can plausibly increase capacity. Within this approach, we developed an architecture and converted logical models into physical resource models and associated design features which were used to build a simulator. From this simulator, we are able to analyse mixtures of legacy-autonomous operations and produce fundamental diagrams and trajectory plots to describe the dynamic behaviour of mixed mainline railway operations.Keywords: autonomy, executable architecture, modelling and simulation, railway capacity
Procedia PDF Downloads 816931 Initial Concept of Islamic Social Entrepreneurship: Identification of Research Gap from Existing Model
Authors: Mohd Adib Abd Muin
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Social entrepreneurship has become a new phenomenon in a country in order to reduce social problems and eradicate poverty communities. However, the study based on Islamic social entrepreneurship from the social entrepreneurial activity is still new especially in the Islamic perspective. In addition, this research found that is lacking of model on social entrepreneurship that focus on Islamic perspective. Therefore, the objective of this paper is to identify the issues and research gap based on Islamic perspective from existing models and to develop a concept of Islamic social entrepreneurship according to Islamic perspective and Maqasid Shari’ah. The research method used in this study is literature review and comparative analysis from 11 existing models of social entrepreneurship. The research finding shows that 11 existing models on social entrepreneurship has been analyzed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.Keywords: component, social entrepreneurship, Islamic perspective, research gap
Procedia PDF Downloads 4486930 Air Quality Analysis Using Machine Learning Models Under Python Environment
Authors: Salahaeddine Sbai
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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.Keywords: air quality, machine learning models, pollution, pollutant emissions
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