Search results for: fast Fourier algorithms

Authors: Randa El Bedawy

Abstract:

Exchanging best practices in the scope of entrepreneurship education and the use of experiential learning approaches are growing lately at a very fast pace. Educators should be challenged to promote such a learning approach to bridge the gap between entrepreneurship students and the actual business work environment. The study aims to share best practices, experiences, and knowledge to support entrepreneurship education. The study is exploratory qualitative research based on a case study approach to demonstrate how experiential learning can be used for supporting learning effectiveness in entrepreneurship education through demonstrating a set of fourteen tasks that were used to engage practically the students who were studying a course of entrepreneurship at the American University in Cairo. The study sheds the light on the rational process of using experiential learning to endorse entrepreneurship education through the illustration of each task along with its learning outcomes. The study explores the benefits and obstacles that educators may face when implementing such an experiential approach. The results of the study confirm that developing an experiential learning approach based on constructing a set of well designed practical tasks that complement the overall intended learning outcomes has proven very effective for promoting the students’ learning of entrepreneurship education. However, good preparation for both educators and students is needed primarily to ensure the effective implementation of such an experiential learning approach.

Keywords: business education, entrepreneurship, entrepreneurship education, experiential learning

Procedia PDF Downloads 166

Authors: Abdul Rehman, Bo Liu

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Axial flow turbines are commonly designed with high loads that generate strong secondary flows and result in high secondary losses. These losses contribute to almost 30% to 50% of the total losses. Non-axisymmetric endwall profiling is one of the passive control technique to reduce the secondary flow loss. In this paper, the non-axisymmetric endwall profile construction and optimization for the stator endwalls are presented to improve the efficiency of a high pressure turbine. The commercial code NUMECA Fine/ Design3D coupled with Fine/Turbo was used for the numerical investigation, design of experiments and the optimization. All the flow simulations were conducted by using steady RANS and Spalart-Allmaras as a turbulence model. The non-axisymmetric endwalls of stator hub and shroud were created by using the perturbation law based on Bezier Curves. Each cut having multiple control points was supposed to be created along the virtual streamlines in the blade channel. For the design of experiments, each sample was arbitrarily generated based on values automatically chosen for the control points defined during parameterization. The Optimization was achieved by using two algorithms i.e. the stochastic algorithm and gradient-based algorithm. For the stochastic algorithm, a genetic algorithm based on the artificial neural network was used as an optimization method in order to achieve the global optimum. The evaluation of the successive design iterations was performed using artificial neural network prior to the flow solver. For the second case, the conjugate gradient algorithm with a three dimensional CFD flow solver was used to systematically vary a free-form parameterization of the endwall. This method is efficient and less time to consume as it requires derivative information of the objective function. The objective function was to maximize the isentropic efficiency of the turbine by keeping the mass flow rate as constant. The performance was quantified by using a multi-objective function. Other than these two classifications of the optimization methods, there were four optimizations cases i.e. the hub only, the shroud only, and the combination of hub and shroud. For the fourth case, the shroud endwall was optimized by using the optimized hub endwall geometry. The hub optimization resulted in an increase in the efficiency due to more homogenous inlet conditions for the rotor. The adverse pressure gradient was reduced but the total pressure loss in the vicinity of the hub was increased. The shroud optimization resulted in an increase in efficiency, total pressure loss and entropy were reduced. The combination of hub and shroud did not show overwhelming results which were achieved for the individual cases of the hub and the shroud. This may be caused by fact that there were too many control variables. The fourth case of optimization showed the best result because optimized hub was used as an initial geometry to optimize the shroud. The efficiency was increased more than the individual cases of optimization with a mass flow rate equal to the baseline design of the turbine. The results of artificial neural network and conjugate gradient method were compared.

Keywords: artificial neural network, axial turbine, conjugate gradient method, non-axisymmetric endwall, optimization

Procedia PDF Downloads 226

Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

Abstract:

The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

Procedia PDF Downloads 308

Authors: M. D. D. Chathurangi, M. G. K. Nayanathara, K. M. H. M. M. Gunapala, G. M. R. G. Dayananda, Kavinga Yapa Abeywardena, Deemantha Siriwardana

Abstract:

Due to the growing popularity of social media platforms at present, there are various concerns, mostly cyberbullying, spam, bot accounts, and the spread of incorrect information. To develop a risk score calculation system as a thorough method for deciphering and exposing unethical social media profiles, this research explores the most suitable algorithms to our best knowledge in detecting the mentioned concerns. Various multiple models, such as Naïve Bayes, CNN, KNN, Stochastic Gradient Descent, Gradient Boosting Classifier, etc., were examined, and the best results were taken into the development of the risk score system. For cyberbullying, the Logistic Regression algorithm achieved an accuracy of 84.9%, while the spam-detecting MLP model gained 98.02% accuracy. The bot accounts identifying the Random Forest algorithm obtained 91.06% accuracy, and 84% accuracy was acquired for fake news detection using SVM.

Keywords: cyberbullying, spam behavior, bot accounts, fake news, machine learning

Procedia PDF Downloads 43

Authors: Shahnaz Mansouri, David Martin, Xiao Dong Chen, Meng Wai Woo

Abstract:

Ultrafine hydrophobic and non-water-soluble drugs can increase the percentage of absorbed compared to their initial dosage. This paper provides a scalable new method of making ultrafine particles of substantially insoluble water compounds specifically, submicron particles of ethanol soluble and water insoluble pharmaceutical materials by steaming an ethanol droplet to prepare a suspension and then followed by immediate drying. This suspension is formed by adding evaporated water molecules as an anti-solvent to the solute of the samples and in early stage of precipitation continued to dry by evaporating both solvent and anti-solvent. This fine particle formation has produced fast dispersion powder in water. The new method is an extension of the antisolvent vapour precipitation technique which exposes a droplet to an antisolvent vapour with reference to the dissolved materials within the droplet. Ultrafine vitamin D3 and ibuprofen particles in the submicron ranges were produced. This work will form the basis for using spray dryers as high-throughput scalable micro-precipitators.

Keywords: single droplet drying, nano size particles, non-water-soluble drugs, precipitators

Procedia PDF Downloads 486

Authors: Sirapop Nuannimnoi, Hojjat Baghban, Ching-Yao Huang

Abstract:

Over the past decade, with the fast development of blockchain technology since the birth of Bitcoin, there has been a massive increase in the usage of Cryptocurrencies. Cryptocurrencies are not seen as an investment opportunity due to the market’s erratic behavior and high price volatility. With the recent success of deep reinforcement learning (DRL), portfolio management can be modeled and automated. In this paper, we propose a novel DRL-based multi-agent system to automatically make proper trading decisions on multiple cryptocurrencies and gain profits in the highly volatile cryptocurrency market. We also extend this multi-agent system with horizontal federated transfer learning for better adapting to the inclusion of new cryptocurrencies in our portfolio; therefore, we can, through the concept of diversification, maximize our profits and minimize the trading risks. Experimental results through multiple simulation scenarios reveal that this proposed algorithmic trading system can offer three promising key advantages over other systems, including maximized profits, minimized risks, and adaptability.

Keywords: cryptocurrency portfolio management, algorithmic trading, federated learning, multi-agent reinforcement learning

Procedia PDF Downloads 121

Authors: Aarti Suryawanshi

Abstract:

Royal Tourism has always been a popular niche activity for many tourists around the world. The United Kingdom being at the heart of it, has been a pioneering nation for Royal tourists. Though many other countries with monarchies such as India, Thailand, Japan, Spain, Netherlands, and many more have attracted tourists with the motivation to see and experience the royalty to their nations, the Middle Eastern countries have never really been the attraction for Royal tourists. Royalty in the middle east is fast emerging as a tourist product and also paving way to marketing opportunity that may lead to the increased popularity of the Royal Houses of the region. Dubai has been garnering the centre stage for futuristic developments, economic growth initiatives, and continuous efforts towards urbanisation which has brought the lime light on the Royal house of the Al Maktoum globally, along with the younger royal members being extensively recognised and appreciated for their public and private adventures which are shared through various social media platforms. The objective of this paper is to analyse the popularity of His Highness Sheikh Hamdan Bin Mohammed Bin Rashid Al Maktoum through social media platforms and the possibility of inducing Royal Tourism in Dubai. An empirical study has been performed to describe the automated repositioning of the city of Dubai as a royal tourism hub.

Keywords: royalty, royal tourism, monarchy, marketing strategy, repositioning

Procedia PDF Downloads 102

Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

Procedia PDF Downloads 327

Authors: Yuanjin Liu

Abstract:

Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model.

Keywords: ruin probability, retirement withdrawal strategies, predictive models, optimal model

Procedia PDF Downloads 79

Authors: Samuel K. Degife, Kamal K. Pant, Sapna Jain

Abstract:

The world´s population and industrialization of countries continued to grow in an alarming rate irrespective of the security for food, energy supply, and pure water availability. As a result, the global energy consumption is observed to increase significantly. Fossil energy resources that mainly comprised of crude oil, coal, and natural gas have been used by mankind as the main energy source for almost two centuries. However, sufficient evidences are revealing that the consumption of fossil resource as transportation fuel emits environmental pollutants such as CO2, NOx, and SOx. These resources are dwindling rapidly besides enormous amount of problems associated such as fluctuation of oil price and instability of oil-rich regions. Biomass is a promising renewable energy candidate to replace fossil-based transportation fuel and chemical production. The present study aims at valorization of hexose sugars (glucose and fructose) using zeolite based catalysts in imidazolium based ionic liquid (1-butyl-3-methylimidazolium chloride, [BMIM] Cl) reaction media. The catalytic effect chromium impregnated H-ZSM-5 (Cr/H-ZSM-5) was studied for dehydration of hexose sugars. The wet impregnation method was used to prepare Cr/H-ZSM-5 catalyst. The characterization of the prepared catalyst was performed using techniques such as Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction analysis (XRD), Temperature-programmed desorption of ammonia (NH3-TPD) and BET-surface area analysis. The dehydration product, 5-hydroxymethylfurfural (5-HMF), was analyzed using high-performance liquid chromatography (HPLC). Cr/H-ZSM-5 was effective in dehydrating fructose with 87% conversion and 55% yield 5-HMF at 180 oC for 30 min of reaction time compared with H-ZSM-5 catalyst which yielded only 31% of 5-HMF at identical reaction condition.

Keywords: chromium, hexose, ionic liquid, , zeolite

Procedia PDF Downloads 178

Authors: Lea Lenhardt, Ivana Zeković, Tatjana Dramićanin, Miroslav D. Dramićanin

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Rapid and sensitive analytical technologies for food analysis are needed to respond to the growing public interest in food quality and safety. In this context, fluorescence spectroscopy offers several inherent advantages for the characterization of food products: high sensitivity, low price, objective, relatively fast and non-destructive. The objective of this work was to investigate the potential of fluorescence spectroscopy coupled with multi-way technique for characterization of cereal flours. Fluorescence landscape also known as excitation-emission matrix (EEM) spectroscopy utilizes multiple-color illumination, with the full fluorescence spectrum recorded for each excitation wavelength. EEM was measured on various types of cereal flours (wheat, oat, barley, rye, corn, buckwheat and rice). Obtained spectra were analyzed using PARAllel FACtor analysis (PARAFAC) in order to decompose the spectra and identify underlying fluorescent components. Results of the analysis indicated the presence of four fluorophores in cereal flours. It has been observed that relative concentration of fluorophores varies between different groups of flours. Based on these findings we can conclude that application of PARAFAC analysis on fluorescence data is a good foundation for further qualitative analysis of cereal flours.

Keywords: cereals, fluors, fluorescence, PARAFAC

Procedia PDF Downloads 668

Authors: Lucas Kurth de Azambuja, Loise Silveira da Silva, Gean Vitor Salmoria, Darlan Dallacosta, Carlos Rodrigo de Mello Roesler

Abstract:

This work provides a physicochemical and thermal characterization assessment of three different hyaluronic acid (HA)-based injections used for rejuvenation skin treatments. The three products analyzed are manufactured by the same manufacturer and commercialized for application on different skin levels. According to the manufacturer, all three HA-based injections are crosslinked and have a concentration of 23 mg/mL of HA, and 0.3% of lidocaine. Samples were characterized by Fourier-transformed infrared (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and scanning electron microscope (SEM) techniques. FTIR analysis resulted in a similar spectrum when comparing the different products. DSC analysis demonstrated that the fusion points differ in each product, with a higher fusion temperature observed in specimen A, which is used for subcutaneous applications, when compared with B and C, which are used for the middle dermis and deep dermis, respectively. TGA data demonstrated a considerable mass loss at 100°C, which means that the product has more than 50% of water in its composition. TGA analysis also showed that Specimen A had a lower mass loss at 100°C when compared to Specimen C. A mass loss of around 220°C was observed on all samples, characterizing the presence of hyaluronic acid. SEM images displayed a similar structure on all samples analyzed, with a thicker layer for Specimen A when compared with B and C. This series of analyses demonstrated that, as expected, the physicochemical and thermal properties of the products differ according to their application. Furthermore, to better characterize the crosslinking degree of each product and their mechanical properties, a set of different techniques should be applied in parallel to correlate the results and, thereby, relate injection application with material properties.

Keywords: hyaluronic acid, characterization, soft-tissue fillers, injectable gels

Procedia PDF Downloads 92

Authors: U.V. Suryawanshi, S.S. Chowhan, U.V Kulkarni

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Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper portraits performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI has been developed. The objective of this paper is to perform a segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. All methods are applied on MRI brain images which are degraded by salt-pepper noise demonstrate that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with a discussion on the trend of future research in brain segmentation and changing norms in IFCM for better results.

Keywords: image segmentation, preprocessing, MRI, FCM, KFCM, SFCM, IFCM

Procedia PDF Downloads 335

Authors: Ian Marc G. Cabugsa, Kim Ryan A. Won

Abstract:

Fast, reliable and simultaneous HPLC analysis of theobromine and caffeine in cacao and cocoa products was optimized in this study. The samples tested were raw, fermented, and roasted cacao beans as well as commercially available cocoa products. The HPLC analysis was carried out using step gradient solvent system with acetonitrile and water buffered with H3PO4 as the mobile phase. The HPLC system was optimized using 273 nm wavelength at 35 °C for the column temperature with a flow rate of 1.0 mL/min. Using this method, the theobromine percent recovery mean, Limit of Detection (LOD) and Limit of Quantification (LOQ) is 118.68(±3.38)%, 0.727 and 1.05 respectively. The percent recovery mean, LOD and LOQ for caffeine is 105.53(±3.25)%, 2.42 and 3.50 respectively. The inter-day and intra-day precision for theobromine is 4.31% and 4.48% respectively, while 7.02% and 7.03% was for caffeine respectively. Compared to the standard method in AOAC using methanol in isocratic solvent system, the results of the study produced lesser chromatogram noise with emphasis on theobromine and caffeine. The method is readily usable for cacao and cocoa substances analyses using HPLC with step gradient capability.

Keywords: cacao, caffeine, HPLC, step gradient solvent system, theobromine

Procedia PDF Downloads 285

Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

Abstract:

An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electromechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

Procedia PDF Downloads 258

Authors: Rafat Alwazna

Abstract:

Technical lexicon is witnessing a large upsurge in the use of new terminologies whose emergence is an inevitable result of the spread of high-quality technology, the existence of scientific paradigms and the fast growth of research in different disciplines. One important subfield of terminology is legal terminology, which forms a crucial part of legal studies, and whose translation from one legal system into another is deemed a formidable and arduous task that needs to be properly performed by legal translators. Indeed, the issue of untranslatability of legal terms, particularly between originally unrelated languages, like legal Arabic and legal English, has long been a real challenge in legal translation. It stems from the conceptual incongruency between legal terms of different legal languages, which are derived from different legal cultures and legal systems. Such conceptual asymmetry is owing to the fact that law has no universal reference and that legal language is what determines the degree of difference in conceptual correspondence. The present paper argues that although conceptual asymmetry, which is the main reason for the issue of untranslatability of legal terms, cannot be denied in legal translation, there exist certain translation techniques which, if properly adopted, would resolve the issue of untranslatability of legal terms and therefore achieve acceptable legal translation. Hence, the question of untranslatability of legal terms should no longer exist within the context of legal translation.

Keywords: conceptual incongruency, Legal terms, translation techniques, untranslatability

Procedia PDF Downloads 205

Authors: Ukpaka Paschal

Abstract:

Margret Boden argues that a creative product is one that is new, surprising, and valuable as a result of the combination, exploration, or transformation involved in producing it. Boden uses examples of artificial intelligence systems that fit all of these criteria and argues that real creativity involves autonomy, intentionality, valuation, emotion, and consciousness. This paper provides an analysis of all these elements in order to try to understand whether they are sufficient to account for creativity, especially human creativity. This paper focuses on Generative Adversarial Networks (GANs), which is a class of artificial intelligence algorithms that are said to have disproved the common perception that creativity is something that only humans possess. This paper will then argue that Boden’s listed properties of creativity, which capture the creativity exhibited by GANs, are not sufficient to account for human creativity, and this paper will further identify “first-person phenomenological experience” as an essential property of human creativity. The rationale behind the proposed essential property is that if creativity involves comprehending our experience of the world around us into a form of self-expression, then our experience of the world really matters with regard to creativity.

Keywords: artificial intelligence, creativity, GANs, first-person experience

Procedia PDF Downloads 142

Authors: Berhane Tewelday Weldhiwot

Abstract:

This research investigates the pivotal role of modular forms in modern cryptographic systems, particularly focusing on their applications in secure communications and data integrity. Modular forms, which are complex analytic functions with rich arithmetic properties, have gained prominence due to their connections to number theory and algebraic geometry. This study begins by outlining the fundamental concepts of modular forms and their historical development, followed by a detailed examination of their applications in cryptographic protocols such as elliptic curve cryptography and zero-knowledge proofs. By employing techniques from analytic number theory, the research delves into how modular forms can enhance the efficiency and security of cryptographic algorithms. The findings suggest that leveraging modular forms not only improves computational performance but also fortifies security measures against emerging threats in digital communication. This work aims to contribute to the ongoing discourse on integrating advanced mathematical theories into practical applications, ultimately fostering innovation in cryptographic methodologies.

Keywords: modular forms, cryptography, elliptic curves, applications, mathematical theory

Procedia PDF Downloads 27

Authors: O. I. H. Dimitry, N. A. Mansour, A. L. G. Saad

Abstract:

Natural sodium montmorillonite (NaMMT), Cloisite Na⁺ and two organophilic montmorillonites (OMMTs), Cloisites 20A and 15A were used. Polycaprolactone (PCL)/MMT composites containing 1, 3, 5, and 10 wt% of Cloisite Na⁺ and PCL/OMMT nanocomposites containing 5 and 10 wt% of Cloisites 20A and 15A were prepared via solution intercalation technique to study the influence of organic modifier loading on particle dispersion of PCL/ NaMMT composites. Thermal stabilities of the obtained composites were characterized by thermal analysis using the thermogravimetric analyzer (TGA) which showed that in the presence of nitrogen flow the incorporation of 5 and 10 wt% of filler brings some decrease in PCL thermal stability in the sequence: Cloisite Na+>Cloisite 15A > Cloisite 20A, while in the presence of air flow these fillers scarcely influenced the thermoxidative stability of PCL by slightly accelerating the process. The interaction between PCL and silicate layers was studied by Fourier transform infrared (FTIR) spectroscopy which confirmed moderate interactions between nanometric silicate layers and PCL segments. The electrical conductivity (σ) which describes the ionic mobility of the systems was studied as a function of temperature and showed that σ of PCL was enhanced on increasing the modifier loading at filler content of 5 wt%, especially at higher temperatures in the sequence: Cloisite Na⁺<Cloisite 20A<Cloisite 15A, and was then decreased to some extent with a further increase to 10 wt%. The activation energy E_σ obtained from the dependency of σ on temperature using Arrhenius equation was found to be lowest for the nanocomposite containing 5 wt% of Cloisite 15A. The dispersed behavior of clay in PCL matrix was evaluated by X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses which revealed partial intercalated structures in PCL/NaMMT composites and semi-intercalated/semi-exfoliated structures in PCL/OMMT nanocomposites containing 5 wt% of Cloisite 20A or Cloisite 15A.

Keywords: electrical conductivity, montmorillonite, nanocomposite, organoclay, polycaprolactone

Procedia PDF Downloads 379

Authors: Areej M. Abduldaim, Nadia M. G. Al-Saidi

Abstract:

Algebra is one of the important fields of mathematics. It concerns with the study and manipulation of mathematical symbols. It also concerns with the study of abstractions such as groups, rings, and fields. Due to the development of these abstractions, it is extended to consider other structures, such as vectors, matrices, and polynomials, which are non-numerical objects. Computer algebra is the implementation of algebraic methods as algorithms and computer programs. Recently, many algebraic cryptosystem protocols are based on non-commutative algebraic structures, such as authentication, key exchange, and encryption-decryption processes are adopted. Cryptography is the science that aimed at sending the information through public channels in such a way that only an authorized recipient can read it. Ring theory is the most attractive category of algebra in the area of cryptography. In this paper, we employ the algebraic structure called skew -Armendariz rings to design a neoteric algorithm for zero knowledge proof. The proposed protocol is established and illustrated through numerical example, and its soundness and completeness are proved.

Keywords: cryptosystem, identification, skew π-Armendariz rings, skew polynomial rings, zero knowledge protocol

Procedia PDF Downloads 221

Authors: Okafor E. Ijeoma, Isimi C. Yetunde, Okoh E. Judith, Kunle O. Olobayo, Emeje O. Martins

Abstract:

Starch has gained interest as a renewable and environmentally compatible polymer due to the increase in its use. However, starch by itself could not be satisfactorily applied in industrial processes due to some inherent disadvantages such as its hydrophilic character, poor mechanical properties, its inability to withstand processing conditions such as extreme temperatures, diverse pH, high shear rate, freeze-thaw variation and dimensional stability. The range of physical properties of parent starch can be enlarged by chemical modification which invariably enhances their use in a number of applications found in industrial processes and food manufacture. In this study, Manihot esculentus starch was subjected to modification by oxidation. Fourier Transmittance Infra- Red (FTIR) and Raman spectroscopies were used to confirm the synthesis while Scanning Electron Microscopy (SEM) and X- Ray Diffraction (XRD) were used to characterize the new polymer. DPPH (2, 2-diphenyl-1-picryl-hydrazyl-hydrate) free radical assay was used to determine the antioxidant property of the oxidized starch. Our results show that the modification had no significant effect on the foaming capacity as well as on the emulsion capacity. Scanning electron microscopy revealed that oxidation did not alter the predominantly circular-shaped starch granules, while the X-ray pattern of both starch, native and modified were similar. FTIR results revealed a new band at 3007 and 3283cm-1. Differential scanning calorimetry returned two new endothermic peaks in the oxidized starch with an improved gelation capacity and increased enthalpy of gelatinization. The IC50 of oxidized starch was notably higher than that of the reference standard, ascorbic acid.

Keywords: antioxidant activity, DPPH, M. esculentus, oxidation, starch

Procedia PDF Downloads 301

Authors: S. Vignesh, K. S. Rangasamy

Abstract:

The paper deals with the device capable of detecting and disabling digital cameras. The system locates the camera and then neutralizes it. Every digital camera has an image sensor known as a CCD, which is retro-reflective and sends light back directly to its original source at the same angle. The device shines infrared LED light, which is invisible to the human eye, at a distance of about 20 feet. It then collects video of these reflections with a camcorder. Then the video of the reflections is transferred to a computer connected to the device, where it is sent through image processing algorithms that pick out infrared light bouncing back. Once the camera is detected, the device would project an invisible infrared laser into the camera's lens, thereby overexposing the photo and rendering it useless. Low levels of infrared laser neutralize digital cameras but are neither a health danger to humans nor a physical damage to cameras. We also discuss the simplified design of the above device that can used in theatres to prevent piracy. The domains being covered here are optics and image processing.

Keywords: CCD, optics, image processing, D3CIP

Procedia PDF Downloads 360

Authors: Monu Verma, Hyunook Kim

Abstract:

Heavy metals and organic dyes are the major sources of water pollution. Herein, a trifunctional β−cyclodextrin−ethylenediaminetetraacetic acid−chitosan (β−CD−EDTA−CS) polymer was synthesized using an easy and simple chemical route by the reaction of activated β−CD with CS through EDTA as a cross-linker (amidation reaction) for the removal of inorganic and organic pollutants from aqueous solution under different parameters such as pH, time effect, initial concentration, reusability, etc. The synthesized adsorbent was characterized using powder X-ray diffraction, Fourier transform infrared spectroscopy, field scanning electron microscopy, energy dispersive spectroscopy, Brunauer-Emmett-Teller (BET), thermogravimetric analyzer techniques to investigate their structural, functional, morphological, elemental compositions, surface area, and thermal properties, respectively. Two types of heavy metals, i.e., mercury (Hg²⁺) and cadmium (Cd²⁺), and three organic dyes, i.e., methylene blue (MB), crystal violet (CV), and safranin O (SO), were chosen as inorganic and organic pollutants, respectively, to study the adsorption capacity of β-CD-EDTA-CS in aqueous solution. The β-CD-EDTA-CS shows a monolayer adsorption capacity of 346.30 ± 14.0 and 202.90 ± 13.90 mg g−¹ for Hg²⁺ and Cd²⁺, respectively, and a heterogeneous adsorption capacity of 107.20 ± 5.70, 77.40 ± 5.30 and 55.30 ± 3.60 mg g−¹ for MB, CV and SO, respectively. Kinetics results followed pseudo-second order (PSO) kinetics behavior for both metal ions and dyes, and higher rate constants values (0.00161–0.00368 g mg−¹ min−¹) for dyes confirmed the cavitation of organic dyes (physisorption). In addition, we have also demonstrated the performance of β-CD-EDTA-CS for the four heavy metals, Hg²⁺, Cd²⁺, Ni²⁺, and Cu²⁺, and three dyes MB, CV, and SO in secondary treated wastewater. The findings of this study indicate that β-CD-EDTA-CS is simple and easy to synthesize and can be used in wastewater treatment.

Keywords: adsorption isotherms, adsorption mechanism, amino-β-cyclodextrin, heavy metal ions, organic dyes

Procedia PDF Downloads 112

Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

Abstract:

An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electro-mechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

Procedia PDF Downloads 295

Authors: Belkacem Laimouche

Abstract:

With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: E. Gil, A. Zepeda, J. Rivera, C. Ben-Youssef, S. Rincón

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Water pollution has become one of the most serious environmental problems. Multiple methods have been proposed for the removal of Pb(II) from contaminated water. Among these, adsorption processes have shown to be more efficient, cheaper and easier to handle with respect to other treatment methods. However, research for adsorbents with high adsorption capacities is still necessary. For this purpose, we proposed in this work the study of metal-organic Framework [Co2(btec)(bipy)(DMF)2]n (MOF-Co) as adsorbent material of Pb (II) in aqueous media. MOF-Co was synthesized by a simple method. Firstly 4, 4’ dipyridyl, 1,2,4,5 benzenetetracarboxylic acid, cobalt (II) and nitrate hexahydrate were first mixed each one in N,N dimethylformamide (DMF) and then, mixed in a reactor altogether. The obtained solution was heated at 363 K in a muffle during 68 h to complete the synthesis. It was washed and dried, obtaining MOF-Co as the final product. MOF-Co was characterized before and after the adsorption process by Fourier transforms infrared spectra (FTIR) and X-ray photoelectron spectroscopy (XPS). The Pb(II) in aqueous media was detected by Absorption Atomic Spectroscopy (AA). In order to evaluate the adsorption process in the presence of Pb(II) in aqueous media, the experiments were realized in flask of 100 ml the work volume at 200 rpm, with different MOF-Co quantities (0.0125 and 0.025 g), pH (2-6), contact time (0.5-6 h) and temperature (298,308 and 318 K). The kinetic adsorption was represented by pseudo-second order model, which suggests that the adsorption took place through chemisorption or chemical adsorption. The best adsorption results were obtained at pH 5. Langmuir, Freundlich and BET equilibrium isotherms models were used to study the adsorption of Pb(II) with 0.0125 g of MOF-Co, in the presence of different concentration of Pb(II) (20-200 mg/L, 100 mL, pH 5) with 4 h of reaction. The correlation coefficients (R2) of the different models show that the Langmuir model is better than Freundlich and BET model with R2=0.97 and a maximum adsorption capacity of 833 mg/g. Therefore, the Langmuir model can be used to best describe the Pb(II) adsorption in monolayer behavior on the MOF-Co. This value is the highest when compared to other materials such as the graphene/activated carbon composite (217 mg/g), biomass fly ashes (96.8 mg/g), PVA/PAA gel (194.99 mg/g) and MOF with Ag12 nanoparticles (120 mg/g).

Keywords: adsorption, heavy metals, metal-organic frameworks, Pb(II)

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Authors: Smail Boussaadi, Hassina Aliane

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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.

Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model

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Authors: Muhammad Zahid, Ilker S. Bayer, Athanassia Athanassiou

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Fluorinated polymers having C8 chemistry (chemicals with 8 fluorinated carbon atoms) are well renowned for their excellent low surface tension and water repelling properties. However, these polymers degrade into highly toxic heavy perfluoro acids in the environment. When the C8 chemistry is reduced to C6 chemistry, this environmental concern is eliminated at the expense of reduced liquid repellent performance. In order to circumvent this, in this study, we demonstrate pre-treatment of woven cotton fabrics with a fluorinated acrylic copolymer with C6 chemistry and subsequently with a silicone polymer to render them hydrophobic. A commercial fluorinated acrylic copolymer was blended with silica nanoparticles to form hydrophobic nano-roughness on cotton fibers and a second coating layer of polydimethylsiloxane (PDMS) was applied on the fabric. A static water contact angle (for 5µl) and rolling angle (for 12.5µl) of 147°±2° and 31° were observed, respectively. Hydrostatic head measurements were also performed to better understand the performance with 26±1 cm and 2.56kPa column height and static pressure respectively. Fabrication methods (with rod coater etc.) were kept simple, reproducible, and scalable and cost efficient. Moreover, the robustness of applied coatings was also evaluated by sonication cleaning and abrasion methods. Water contact angle (WCA), water shedding angle (WSA), hydrostatic head, droplet bouncing-rolling off and prolonged staining tests were used to characterize hydrophobicity of materials. For chemical and morphological analysis, various characterization methods were used such as attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), atomic force microscopy (AFM) and scanning electron microscopy (SEM).

Keywords: fluorinated polymer, hydrophobic, polydimethylsiloxane, water contact angle

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Authors: Surita Maini, Sanjay Dhanka

Abstract:

Machine learning (ML) involves developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed. Because of its unlimited abilities ML is gaining popularity in medical sectors; Medical Imaging, Electronic Health Records, Genomic Data Analysis, Wearable Devices, Disease Outbreak Prediction, Disease Diagnosis, etc. In the last few decades, many researchers have tried to diagnose Breast Cancer (BC) using ML, because early detection of any disease can save millions of lives. Working in this direction, the authors have proposed a hybrid ML technique RBF SVM, to predict the BC in earlier the stage. The proposed method is implemented on the Breast Cancer UCI ML dataset with 569 instances and 32 attributes. The authors recorded performance metrics of the proposed model i.e., Accuracy 98.24%, Sensitivity 98.67%, Specificity 97.43%, F1 Score 98.67%, Precision 98.67%, and run time 0.044769 seconds. The proposed method is validated by K-Fold cross-validation.

Keywords: breast cancer, support vector classifier, machine learning, hyper parameter tunning

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

Abstract:

Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

Procedia PDF Downloads 421