Search results for: structural vector autoregression

Authors: Xiaoyong He

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The manipulation of THz waves is still a challenging task due to lack of natural materials interacted with it strongly. Designed by tailoring the characters of unit cells (meta-molecules), the advance of metamaterials (MMs) may solve this problem. However, because of Ohmic and radiation losses, the performance of MMs devices is subjected to the dissipation and low quality factor (Q-factor). This dilemma may be circumvented by Fano resonance, which arises from the destructive interference between a bright continuum mode and dark discrete mode (or a narrow resonance). Different from symmetric Lorentz spectral curve, Fano resonance indicates a distinct asymmetric line-shape, ultrahigh quality factor, steep variations in spectrum curves. Fano resonance is usually realized through symmetry breaking. However, if concentric double rings (DR) are placed closely to each other, the near-field coupling between them gives rise to two hybridized modes (bright and narrowband dark modes) because of the local asymmetry, resulting into the characteristic Fano line shape. Furthermore, from the practical viewpoint, it is highly desirable requirement that to achieve the modulation of Fano spectral curves conveniently, which is an important and interesting research topics. For current Fano systems, the tunable spectral curves can be realized by adjusting the geometrical structural parameters or magnetic fields biased the ferrite-based structure. But due to limited dispersion properties of active materials, it is still a tough work to tailor Fano resonance conveniently with the fixed structural parameters. With the favorable properties of extreme confinement and high tunability, graphene is a strong candidate to achieve this goal. The DR-structure possesses the excitation of so-called “trapped modes,” with the merits of simple structure and high quality of resonances in thin structures. By depositing graphene circular DR on the SiO2/Si/ polymer substrate, the tunable Fano resonance has been theoretically investigated in the terahertz regime, including the effects of graphene Fermi level, structural parameters and operation frequency. The results manifest that the obvious Fano peak can be efficiently modulated because of the strong coupling between incident waves and graphene ribbons. As Fermi level increases, the peak amplitude of Fano curve increases, and the resonant peak position shifts to high frequency. The amplitude modulation depth of Fano curves is about 30% if Fermi level changes in the scope of 0.1-1.0 eV. The optimum gap distance between DR is about 8-12 μm, where the value of figure of merit shows a peak. As the graphene ribbon width increases, the Fano spectral curves become broad, and the resonant peak denotes blue shift. The results are very helpful to develop novel graphene plasmonic devices, e.g. sensors and modulators.

Keywords: graphene, metamaterials, terahertz, tunable

Procedia PDF Downloads 344

Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

Procedia PDF Downloads 57

Authors: H. Aripin, I. Made Joni, Edvin Priatna, Nundang Busaeri, Svilen Sabchevski

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In this investigation, manganese has been produced from medium grade manganese ore from Karangnunggal mine (West Java, Indonesia). The ores were grinded using a jar mill to pass through a 150 mesh sieve. The effects of keeping it at a temperature of 1200 °C in methane gas on the structural properties have been studied. The material’s properties have been characterized on the basis of the experimental data obtained using X-ray fluorescence (XRF), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier transform infrared (FTIR) spectroscopy. It has been found that the ore contains MnO₂ as the main constituents at about 46.80 wt.%. It can be also observed that the ore particles are agglomerated forming dense grains with different texture and morphology. The irregular-shaped grains with dark contrast, the large brighter grains, and smaller grains with bright texture and smooth surfaces are associated with the presence of manganese, calcium, and quartz, respectively. From XRD patterns, MnO₂ is reduced to hausmannite (Mn₃O₄), manganosite (MnO) and manganese carbide (Mn₇C₃). At a temperature of 1200°C the keeping time does not have any effect on the formation of crystals and the crystalline phases remain almost unchanged in the time range from 15 to 90 minutes. An increase of the keeping time up to 45 minutes during the sintering process leads to an increase of the MnO concentration, while at 90 minutes, the concentration decreases. At longer keeping times the excess reaction of the methane gas and manganese oxide in the ore causes an increase of carbon deposition. As a result, it blocks the particle surface and then hinders the reduction process of manganese oxide. From FTIR spectrum allows one to explain that the appearance of C=O stretching mode arises from absorption of atmospheric methane and manganese oxide of the ore. The intensity of this band increases with increasing the keeping time, indicating an increase of carbon deposition on the surface of manganese oxide.

Keywords: manganese, medium grade manganese ore, structural properties, keeping the temperature, carbon deposition

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Authors: Kehinde Damilola Ilesanmi, Dev Datt Tewari

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South Africa is in its post-industrial era moving from the primary and secondary sector to the tertiary sector. The study investigated the impact of the disaggregated energy consumption (coal, oil, and electricity) on the primary, secondary and tertiary sectors of the economy between 1980 and 2012 in South Africa. Using vector error correction model, it was established that South Africa is an energy dependent economy, and that energy (especially electricity and oil) is a limiting factor of growth. This implies that implementation of energy conservation policies may hamper economic growth. Output growth is significantly outpacing energy supply, which has necessitated load shedding. To meet up the excess energy demand, there is a need to increase the generating capacity which will necessitate increased investment in the electricity sector as well as strategic steps to increase oil production. There is also need to explore more renewable energy sources, in order to meet the growing energy demand without compromising growth and environmental sustainability. Policy makers should also pursue energy efficiency policies especially at sectoral level of the economy.

Keywords: causality, economic growth, energy consumption, hypothesis, sectoral output

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Authors: Ákos Wolf, Richard P. Ray

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Due to the introduction of Eurocode 8, the structural design for seismic and dynamic effects has become more significant in Hungary. This has emphasized the need for more effort to describe the behavior of structures under these conditions. Soil conditions have a significant effect on the response of structures by modifying the stiffness and damping of the soil-structural system and by modifying the seismic action as it reaches the ground surface. Shear modulus (G) and shear wave velocity (v_s), which are often measured in the field, are the fundamental dynamic soil properties for foundation vibration problems, liquefaction potential and earthquake site response analysis. There are several laboratory and in-situ measurement techniques to evaluate dynamic soil properties, but unfortunately, they are often too expensive for general design practice. However, a significant number of correlations have been proposed to determine shear wave velocity or shear modulus from Cone Penetration Tests (CPT), which are used more and more in geotechnical design practice in Hungary. This allows the designer to analyze and compare CPT and seismic test result in order to select the best correlation equations for Hungarian soils and to improve the recommendations for the Hungarian geologic conditions. Based on a literature review, as well as research experience in Hungary, the influence of various parameters on the accuracy of results will be shown. This study can serve as a basis for selecting and modifying correlation equations for Hungarian soils. Test data are taken from seven locations in Hungary with similar geologic conditions. The shear wave velocity values were measured by seismic CPT. Several factors are analyzed including soil type, behavior index, measurement depth, geologic age etc. for their effect on the accuracy of predictions. The final results show an improved prediction method for Hungarian soils

Keywords: CPT correlation, dynamic soil properties, seismic CPT, shear wave velocity

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Authors: Angel Pérez Sánchez

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Background: Consider magnetic lines of force as a vector magnetic current was introduced by convention around 1830. But this leads to a dead end in traditional physics, and quantum explanations must be referred to explain the magnetic phenomenon. However, a study of magnetic lines as percussive waves leads to other paths capable of interpreting magnetism through traditional physics. Methodology: Brick used in the experiment: two parallel electric current cables attract each other if current goes in the same direction and its application at a microscopic level inside magnets. Significance: Consideration of magnetic lines as magnets themselves would mean a paradigm shift in the study of magnetism and open the way to provide solutions to mysteries of magnetism until now only revealed by quantum mechanics. Major findings: discover how a magnetic field is created, as well as reason how magnetic attraction and repulsion work, understand how magnets behave when splitting them, and reveal the impossibility of a Magnetic Monopole. All of this is presented as if it were a symphony in which all the notes fit together perfectly to create a beautiful, smart, and simple work.

Keywords: magnetic lines of force, magnetic field, magnetic attraction and repulsion, magnet split, magnetic monopole, magnetic lines of force as magnets, magnetic lines of force as waves

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Authors: Muhammad Aji Fajari, Ririt Aprilin Sumarsono

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Particular instability of structural building under lateral load (e.g earthquake) will rise due to irregularity in vertical and horizontal direction as stated in SNI 03-1762-2012. The conventional slab has been considered for its less contribution in increasing the stability of the structure, except special slab system such as flat slab turned into account. In this paper, the analysis of flat slab system at Sequis Tower located in South Jakarta will be assessed its performance under earthquake. It consists of 6 floors of the basement where the flat slab system is applied. The flat slab system will be the main focus in this paper to be compared for its performance with conventional slab system under earthquake. Regarding the floor plan of Sequis Tower basement, re-entrant corner signed for this building is 43.21% which exceeded the allowable re-entrant corner is 15% as stated in ASCE 7-05 Based on that, the horizontal irregularity will be another concern for analysis, otherwise vertical irregularity does not exist for this building. Flat slab system is a system where the slabs use drop panel with shear head as their support instead of using beams. Major advantages of flat slab application are decreasing dead load of structure, removing beams so that the clear height can be maximized, and providing lateral resistance due to lateral load. Whilst, deflection at middle strip and punching shear are problems to be detail considered. Torsion usually appears when the structural member under flexure such as beam or column dimension is improper in ratio. Considering flat slab as alternative slab system will keep the collapse due to torsion down. Common seismic load resisting system applied in the building is a shear wall. Installation of shear wall will keep the structural system stronger and stiffer affecting in reduced displacement under earthquake. Eccentricity of shear wall location of this building resolved the instability due to horizontal irregularity so that the earthquake load can be absorbed. Performing linear dynamic analysis such as response spectrum and time history analysis due to earthquake load is suitable as the irregularity arise so that the performance of structure can be significantly observed. Utilization of response spectrum data for South Jakarta which PGA 0.389g is basic for the earthquake load idealization to be involved in several load combinations stated on SNI 03-1726-2012. The analysis will result in some basic seismic parameters such as period, displacement, and base shear of the system; besides the internal forces of the critical member will be presented. Predicted period of a structure under earthquake load is 0.45 second, but as different slab system applied in the analysis then the period will show a different value. Flat slab system will probably result in better performance for the displacement parameter compare to conventional slab system due to higher contribution of stiffness to the whole system of the building. In line with displacement, the deflection of the slab will result smaller for flat slab than a conventional slab. Henceforth, shear wall will be effective to strengthen the conventional slab system than flat slab system.

Keywords: conventional slab, flat slab, horizontal irregularity, response spectrum, shear wall

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Authors: Tianyang Xu

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Small underwater targets generally have a non-centrosymmetric geometry, and the acoustic scattering field of the target has spatial inhomogeneity under active sonar detection conditions. In view of the above problems, this paper takes the hemispherical cylindrical shell as the research object, and considers the angle continuity implied in the echo characteristics, and proposes a cluster-driven research method for the non-uniform characteristics of target echo angle. First, the target echo features are extracted, and feature vectors are constructed. Secondly, the t-SNE algorithm is used to improve the internal connection of the feature vector in the low-dimensional feature space and to construct the visual feature space. Finally, the implicit angular relationship between echo features is extracted under unsupervised condition by cluster analysis. The reconstruction results of the local geometric structure of the target corresponding to different categories show that the method can effectively divide the angle interval of the local structure of the target according to the natural acoustic scattering characteristics of the target.

Keywords: underwater target;, non-uniform characteristics;, cluster-driven method;, acoustic scattering characteristics

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: B. S. Abdelwahed, B. B. Belkassem

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Beam-column joints are a critical part in reinforced concrete RC frames designed for inelastic response to several external loads. Investigating the behaviour of the exterior RC beam-column joints has attracted many researchers in the past decades due to its critical influence on the overall behaviour of RC moment-resisting frames subjected to lateral loads. One of the most critical zones in moment-resistant frames is the knee joints because of restraints associated with providing limited anchorage length to the beam and column longitudinal reinforcement in it and consequentially causes a lot of damage in such building frames. Previous numerical simulations focussed mainly on the exterior and interior joints, for knee joint further work is still needed to investigate its behaviour and discuss its affecting parameters. Structural response for an RC knee beam-column joint is performed in this study using LS-DYNA. Three-dimensional finite element (FE) models of an RC knee beam-column joint are described and verified with experimental results available in literature; this is followed by a parametric study to investigate the influence of the concrete compressive strength, the presence of lateral beams and increasing beam reinforcement ratio. It is shown that the concrete compressive strength has a significant effect on shear capacity, load-deflection characteristics and failure modes of an RC knee beam-column joints but to a certain limit, the presence of lateral beams increased the joint confinement and reduced the rate of concrete degradation in the joint after reaching ultimate joint capacity, added to that an increase in the maximum load resistance. Increasing beam reinforcement ratio is found to improve the flexural resistance of the anchored beam bars and increase the joint maximum load resistance.

Keywords: beam reinforcement ratio, joint confinement, numerical simulation, reinforced concrete beam-column joints, structural performance

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Authors: Jeet Chakraborty, Sanjay Dutta

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Antibiotic resistance by bacteria has proved to be a severe threat to mankind in recent times, and this fortifies an urgency to design and develop potent antibacterial small molecules/compounds with nonconventional mechanisms than the conventional ones. DNA carries the genetic signature of any organism, and bacteria maintain their genomic DNA inside the cell in a well-regulated compact form with the help of various nucleoid associated proteins like HU, HNS, etc. These proteins control various fundamental processes like gene expression, replication, etc., inside the cell. Alteration of the native DNA structure of bacteria can lead to severe consequences in cellular processes inside the bacterial cell that ultimately result in the death of the organism. The change in the global DNA structure by small molecules initiates a plethora of cellular responses that have not been very well investigated. Echinomycin and Triostin-A are biologically active Quinoxaline small molecules that typically consist of a quinoxaline chromophore attached with an octadepsipeptide ring. They bind to double-stranded DNA in a sequence-specific way and have high activity against a wide variety of bacteria, mainly against Gram-positive ones. To date, few synthetic quinoxaline scaffolds were synthesized, displaying antibacterial potential against a broad scale of pathogenic bacteria. QNOs (Quinoxaline N-oxides) are known to target DNA and instigate reactive oxygen species (ROS) production in bacteria, thereby exhibiting antibacterial properties. The divergent role of Quinoxaline small molecules in medicinal research qualifies them for the evaluation of their antimicrobial properties as a potential candidate. The previous study from our lab has given new insights on a 6-nitroquinoxaline derivative 1d as an intercalator of DNA, which induces conformational changes in DNA upon binding.7 The binding event observed was dependent on the presence of a crucial benzyl substituent on the quinoxaline moiety. This was associated with a large induced CD (ICD) appearing in a sigmoidal pattern upon the interaction of 1d with dsDNA. The induction of DNA superstructures by 1d at high Drug:DNA ratios was observed that ultimately led to DNA condensation. Eviction of invitro-assembled nucleosome upon treatment with a high dose of 1d was also observed. In this work, monoquinoxaline derivatives of 1d were synthesized by various modifications of the 1d scaffold. The set of synthesized 6-nitroquinoxaline derivatives along with 1d were all subjected to antibacterial evaluation across five different bacteria species. Among the compound set, 3a displayed potent antibacterial activity against Staphylococcus aureus bacteria. 3a was further subjected to various biophysical studies to check whether the DNA structural alteration potential was still intact. The biological response of S. aureus cells upon treatment with 3a was studied using various cell biology processes, which led to the conclusion that 3d can initiate DNA damage in the S. aureus cells. Finally, the potential of 3a in disrupting preformed S.aureus and S.epidermidis biofilms was also studied.

Keywords: DNA structural change, antibacterial, intercalator, DNA superstructures, biofilms

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Authors: Amlan Datta

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Dengue is an arboviral, vector borne infection, quite prevalent in tropical countries such as India. Approximately, 1 to 25% of cases may give rise to neurological complication, such as, seizure, delirium, Guillian-Barre syndrome (GBS), multiple cranial nerve palsies, intracranial thrombosis, stroke-like presentations, to name a few. Dengue fever, as an antecedent to GBS is uncommon, especially in adults.Here, we report a case about a middle aged lady who presented with an acute onset areflexic ascending type of polyradiculoneuropathy along with bilateral lower motor neuron type of facial nerve palsy, as well as abducens and motor component of trigeminal (V3) weakness. Her respiratory and neck muscles were spared. She had an established episode of dengue fever (NS1 and dengue IgM positive) 7 days prior to the weakness. Nerve conduction study revealed a demyelinating polyradiculopathy of both lower limbs and cerebrospinal fluid examination showed albuminocytological dissociation. She was treated with 5 days of intravenous immunoglobulin (IVIg), following which her limb weakness improved considerably. This case highlights GBS as a potential complication following dengue fever.

Keywords: areflexic, demyelinating, dengue, polyradiculoneuropathy

Procedia PDF Downloads 258

Authors: Safia Bashir, Zulfiqar Memon

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During the last decade, renewable energy sources in particular solar photovoltaic (PV) has gained increased attention. Therefore, various PV converters topologies have emerged. Among this topology, the modular multilevel converter (MMC) is considered as one of the most promising topologies for the grid-connected PV system due to its modularity and transformerless features. When it comes to the safe operation of MMC, the balancing of the Submodules Voltages (SMs) plays a critical role. This paper proposes a balancing approach based on space vector PWM (SVPWM). Unlike the existing techniques, this method generates the switching vectors for the MMC by using only one SVPWM for the upper arm. The lower arm switching vectors are obtained by finding the complement of the upper arm switching vectors. The use of one SVPWM not only simplifies the calculation but also helped in reducing the circulating current in the MMC. The proposed method is varied through simulation using Matlab/Simulink and compared with other available modulation methods. The results validate the ability of the suggested method in balancing the SMs capacitors voltages and reducing the circulating current which will help in reducing the power loss of the PV system.

Keywords: capacitor voltage balancing, circulating current, modular multilevel converter, PV system

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Authors: Niharul Alam

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The development of energy efficient, economic and eco-friendly synthetic protocols for metal nanoparticles (NPs) with tailor-made structural properties and biocompatibility is a highly cherished goal for researchers working in the field of nanoscience and nanotechnology. In this context, green chemistry is highly relevant and the 12 principles of Green Chemistry can be explored to develop such synthetic protocols which are practically implementable. One of the most promising green chemical synthetic methods which can serve the purpose is biogenic synthetic protocol, which utilizes non-toxic multifunctional reactants derived from natural, biological sources ranging from unicellular organisms to higher plants that are often characterized as “medicinal plants”. Over the past few years, a plethora of medicinal plants have been explored as the source of this kind of multifunctional green chemical agents. In this presentation, we focus on the syntheses of stable monometallic Au and Ag NPs and also bimetallic Au/Ag alloy NPs with highly efficient catalytic property using aqueous extract of leaves of Indian Curry leaf plat (Murraya koenigii Spreng.; Fam. Rutaceae) as green multifunctional agents which is extensively used in Indian traditional medicine and cuisine. We have also studied the interaction between the synthesized metal NPs and surface-adsorbed fluorescent moieties, quercetin and quercetin glycoside which are its chemical constituents. This helped us to understand the surface property of the metal NPs synthesized by this plant based biogenic route and to predict a plausible mechanistic pathway which may help in fine-tuning green chemical methods for the controlled synthesis of various metal NPs in future. We observed that simple experimental parameters e.g. pH and temperature of the reaction medium, concentration of multifunctional agent and precursor metal ions play important role in the biogenic synthesis of Au NPs with finely tuned structures.

Keywords: green multifunctional agent, metal nanoparticles, biogenic synthesis

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Authors: Laura C. Loaiza, Elodie Salager, Nicolas Louvain, Athmane Boulaoued, Antonella Iadecola, Patrik Johansson, Lorenzo Stievano, Vincent Seznec, Laure Monconduit

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Lithium-ion batteries (LIBs) have an important place among energy storage devices due to their high capacity and good cyclability. However, the advancements in portable and transportation applications have extended the research towards new horizons, and today the development is hampered, e.g., by the capacity of the electrodes employed. Silicon and germanium are among the considered modern anode materials as they can undergo alloying reactions with lithium while delivering high capacities. It has been demonstrated that silicon in its highest lithiated state can deliver up to ten times more capacity than graphite (372 mAh/g): 4200 mAh/g for Li₂₂Si₅ and 3579 mAh/g for Li₁₅Si₄, respectively. On the other hand, germanium presents a capacity of 1384 mAh/g for Li₁₅Ge₄, and a better electronic conductivity and Li ion diffusivity as compared to Si. Nonetheless, the commercialization potential of Ge is limited by its cost. The synergetic effect of Si₁₋ₓGeₓ alloys has been proven, the capacity is increased compared to Ge-rich electrodes and the capacity retention is increased compared to Si-rich electrodes, but the exact performance of this type of electrodes will depend on factors like specific capacity, C-rates, cost, etc. There are several reports on various formulations of Si₁₋ₓGeₓ alloys with promising LIB anode performance with most work performed on complex nanostructures resulting from synthesis efforts implying high cost. In the present work, we studied the electrochemical mechanism of the Si₀.₅Ge₀.₅ alloy as a realistic micron-sized electrode formulation using carboxymethyl cellulose (CMC) as the binder. A combination of a large set of in situ and operando techniques were employed to investigate the structural evolution of Si₀.₅Ge₀.₅ during lithiation and delithiation processes: powder X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), Raman spectroscopy, and 7Li solid state nuclear magnetic resonance spectroscopy (NMR). The results have presented a whole view of the structural modifications induced by the lithiation/delithiation processes. The Si₀.₅Ge₀.₅ amorphization was observed at the beginning of discharge. Further lithiation induces the formation of a-Liₓ(Si/Ge) intermediates and the crystallization of Li₁₅(Si₀.₅Ge₀.₅)₄ at the end of the discharge. At really low voltages a reversible process of overlithiation and formation of Li₁₅₊δ(Si₀.₅Ge₀.₅)₄ was identified and related with a structural evolution of Li₁₅(Si₀.₅Ge₀.₅)₄. Upon charge, the c-Li₁₅(Si₀.₅Ge₀.₅)₄ was transformed into a-Liₓ(Si/Ge) intermediates. At the end of the process an amorphous phase assigned to a-SiₓGey was recovered. Thereby, it was demonstrated that Si and Ge are collectively active along the cycling process, upon discharge with the formation of a ternary Li₁₅(Si₀.₅Ge₀.₅)₄ phase (with a step of overlithiation) and upon charge with the rebuilding of the a-Si-Ge phase. This process is undoubtedly behind the enhanced performance of Si₀.₅Ge₀.₅ compared to a physical mixture of Si and Ge.

Keywords: lithium ion battery, silicon germanium anode, in situ characterization, X-Ray diffraction

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Authors: Carlos Riascos, Peter Thomson

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Real-time hybrid simulation (RTHS) is a modern cyber-physical technique used for the experimental evaluation of complex systems, that treats the system components with predictable behavior as a numerical substructure and the components that are difficult to model as an experimental substructure. Therefore it is an attractive method for evaluation of the response of civil structures under earthquake, wind and anthropic loads. Another practical application of RTHS is the evaluation of control systems, as these devices are often nonlinear and their characterization is an important step in the design of controllers with the desired performance. In this paper, the response of three-story shear frame controlled by a tuned liquid column damper (TLCD) and subject to base excitation is considered. Both passive and semi-active control strategies were implemented and are compared. While the passive TLCD achieved a reduction of 50% in the acceleration response of the main structure in comparison with the structure without control, the semi-active TLCD achieved a reduction of 70%, and was robust to variations in the dynamic properties of the main structure. In addition, a RTHS was implemented with the main structure modeled as a linear, time-invariant (LTI) system through a state space representation and the TLCD, with both control strategies, was evaluated on a shake table that reproduced the displacement of the virtual structure. Current assessment measures for RTHS were used to quantify the performance with parameters such as generalized amplitude, equivalent time delay between the target and measured displacement of the shake table, and energy error using the measured force, and prove that the RTHS described in this paper is an accurate method for the experimental evaluation of structural control systems.

Keywords: structural control, hybrid simulation, tuned liquid column damper, semi-active sontrol strategy

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Authors: Hongyu Chen, Li Jiang

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Ubiquitous anomalies endanger the security of our system con- stantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case, the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.

Keywords: GAN, discrete feature, Wasserstein distance, multiple intermediate layers

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: S. Cherif, A. Medjahed, M. Bouafia, A. Manallah

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A light wave is characterized by 4 characteristics: its amplitude, its frequency, its phase and the direction of polarization of its luminous vector (the electric field). It is in this last characteristic that we will be interested. The polarization of the light was introduced in order to describe the vectorial behavior of the light; it describes the way in which the electric field evolves in a point of space. Our work consists in studying diffusing mediums. Different types of biological fluids were selected to study the evolution of each with increasing scattering power of the medium, and in the same time to make a comparison between them. When crossing these mediums, the light undergoes modifications and/or deterioration of its initial state of polarization. This phenomenon is related to the properties of the medium, the idea is to compare the characteristics of the entering and outgoing light from the studied medium by a white light. The advantage of this model is that it is experimentally accessible workable intensity measurements with CCD sensors and allows operation in 2D. The latter information is used to discriminate some physical properties of the studied areas. We chose four types of milk to study the evolution of each with increasing scattering power of the medium.

Keywords: light polarization, Mueller matrix, Mueller images, diffusing medium, milk

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

Procedia PDF Downloads 263

Authors: Alexander A. Tokmakov

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Cell-free protein synthesis is widely used to synthesize recombinant proteins. It allows genome-scale expression of various polypeptides under strictly controlled uniform conditions. However, only a minor fraction of all proteins can be successfully expressed in the systems of protein synthesis that are currently used. The factors determining expression success are poorly understood. At present, the vast volume of data is accumulated in cell-free expression databases. It makes possible comprehensive bioinformatics analysis and identification of multiple features associated with successful cell-free expression. Here, we describe an approach aimed at identification of multiple physicochemical and structural properties of amino acid sequences associated with protein solubility and aggregation and highlight major correlations obtained using this approach. The developed method includes: categorical assessment of the protein expression data, calculation and prediction of multiple properties of expressed amino acid sequences, correlation of the individual properties with the expression scores, and evaluation of statistical significance of the observed correlations. Using this approach, we revealed a number of statistically significant correlations between calculated and predicted features of protein sequences and their amenability to cell-free expression. It was found that some of the features, such as protein pI, hydrophobicity, presence of signal sequences, etc., are mostly related to protein solubility, whereas the others, such as protein length, number of disulfide bonds, content of secondary structure, etc., affect mainly the expression propensity. We also demonstrated that amenability of polypeptide sequences to cell-free expression correlates with the presence of multiple sites of post-translational modifications. The correlations revealed in this study provide a plethora of important insights into protein folding and rationalization of protein production. The developed bioinformatics approach can be of practical use for predicting expression success and optimizing cell-free protein synthesis.

Keywords: bioinformatics analysis, cell-free protein synthesis, expression success, optimization, recombinant proteins

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Authors: Salah A. Aly, Mohammed A. Ibrahim, Mostafa M. khttab

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The amount of construction and demolition (C&D) waste has increased considerably over the last few decades. From the viewpoint of environmental preservation and effective utilization of resources, crushing C&D concrete waste to produce coarse aggregate (CA) with different replacement percentage for the production of new concrete is one common means for achieving a more environment-friendly concrete. In the study presented herein, the investigation was conducted in two phases. In the first phase, the selection of the materials was carried out and the physical, mechanical and chemical characteristics of these materials were evaluated. Different concrete mixes were designed. The investigation parameter was Recycled Concrete Aggregate (RCA) ratios. The mechanical properties of all mixes were evaluated based on compressive strength and workability results. Accordingly, two mixes have been chosen to be used in the next phase. In the second phase, the study of the structural behavior of the concrete beams was developed. Sixteen beams were casted to investigate the effect of RCA ratios, the shear span to depth ratios and the effect of different locations and reinforcement of openings on the shear behavior of the tested specimens. All these beams were designed to fail in shear. Test results of the compressive strength of concrete indicated that, replacement of natural aggregate by up to 50% recycled concrete aggregates in mixtures with 350 Kg/m3 cement content led to increase of concrete compressive strength. Moreover, the tensile strength and the modulus of elasticity of the specimens with RCA have very close values to those with natural aggregates. The ultimate shear strength of beams with RCA is very close to those with natural aggregates indicating the possibility of using RCA as partial replacement to produce structural concrete elements. The validity of both the Egyptian Code for the design and implementation of Concrete Structures (ECCS) 203-2007 and American Concrete Institute (ACI) 318-2011Codes for estimating the shear strength of the tested RCA beams was investigated. It was found that the codes procedures gives conservative estimates for shear strength.

Keywords: construction and demolition (C&D) waste, coarse aggregate (CA), recycled coarse aggregates (RCA), opening

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Authors: Muhammad Bilal, Adil Ahmed

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This study proposes an approach for electrical induction motor predictive maintenance utilizing machine learning algorithms. On the basis of a study of temperature data obtained from sensors put on the motor, the goal is to predict motor failures. The proposed models are trained to identify whether a motor is defective or not by utilizing machine learning algorithms like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). According to a thorough study of the literature, earlier research has used motor current signature analysis (MCSA) and vibration data to forecast motor failures. The temperature signal methodology, which has clear advantages over the conventional MCSA and vibration analysis methods in terms of cost-effectiveness, is the main subject of this research. The acquired results emphasize the applicability and effectiveness of the temperature-based predictive maintenance strategy by demonstrating the successful categorization of defective motors using the suggested machine learning models.

Keywords: predictive maintenance, electrical induction motors, machine learning, temperature signal methodology, motor failures

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Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

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Authors: R. Haoui

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The purpose of this work is to simulate the flow at the exit of Vulcan 1 engine of European launcher Ariane 5. The geometry of the propellant nozzle is already determined using the characteristics method. The pressure in the outlet section of the nozzle is less than atmospheric pressure on the ground, causing the existence of oblique and normal shock waves at the exit. During the rise of the launcher, the atmospheric pressure decreases and the shock wave disappears. The code allows the capture of shock wave at exit of nozzle. The numerical technique uses the Flux Vector Splitting method of Van Leer to ensure convergence and avoid the calculation instabilities. The Courant, Friedrichs and Lewy coefficient (CFL) and mesh size level are selected to ensure the numerical convergence. The nonlinear partial derivative equations system which governs this flow is solved by an explicit unsteady numerical scheme by the finite volume method. The accuracy of the solution depends on the size of the mesh and also the step of time used in the discretized equations. We have chosen in this study the mesh that gives us a stationary solution with good accuracy.

Keywords: finite volume, lunchers, nozzles, shock wave

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Authors: Carmela Perta

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In Italy the coexistence of standard language, its varieties and different minority languages - historical and migration languages - has been a way to study language contact in different directions; the focus of most of the studies is either the relations among the languages of the social repertoire, or the study of contact phenomena occurring in a particular structural level. However, studies on contact facts in relation to a given sociolinguistic situation of the speech community are still not present in literature. As regard the language level to investigate from the perspective of contact, it is commonly claimed that the lexicon is the most volatile part of language and most likely to undergo change due to superstrate influence, indeed first lexical features are borrowed, then, under long term cultural pressure, structural features may also be borrowed. The aim of this paper is to analyse language contact in two historical minority communities where Francoprovençal is spoken, in relation to their sociolinguistic situation. In this perspective, firstly lexical borrowings present in speakers’ speech production will be examined, trying to find a possible correlation between this part of the lexicon and informants’ sociolinguistic variables; secondly a possible correlation between a particular community sociolinguistic situation and lexical borrowing will be found. Methods used to collect data are based on the results obtained from 24 speakers in both the villages; the speaker group in the two communities consisted of 3 males and 3 females in each of four age groups, ranging in age from 9 to 85, and then divided into five groups according to their occupations. Speakers were asked to describe a sequence of pictures naming common objects and then describing scenes when they used these objects: they are common objects, frequently pronounced and belonging to semantic areas which are usually resistant and which are thought to survive. A subset of this task, involving 19 items with Italian source is examined here: in order to determine the significance of the independent variables (social factors) on the dependent variable (lexical variation) the statistical package SPSS, particularly the linear regression, was used.

Keywords: borrowing, Francoprovençal, language change, lexicon

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Authors: Quynhan Tran, Hong Fan, Quockhanh Pham

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Automatic map generalization is a well-known problem in cartography. The development of map generalization research accompanied the development of cartography. The traditional map is plotted manually by cartographic experts. The paper studies none-scale automation generalization of resident polygons and house marker symbol, proposes methodology to evaluate the result maps based on minimal spanning tree. In this paper, the minimal spanning tree before and after map generalization is compared to evaluate whether the generalization result maintain the geographical distribution of features. The minimal spanning tree in vector format is firstly converted into a raster format and the grid size is 2mm (distance on the map). The statistical number of matching grid before and after map generalization and the ratio of overlapping grid to the total grids is calculated. Evaluation experiments are conduct to verify the results. Experiments show that this methodology can give an objective evaluation for the feature distribution and give specialist an hand while they evaluate result maps of none-scale automation generalization with their eyes.

Keywords: automatic cartography generalization, evaluation model, geographic feature distribution, minimal spanning tree

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Authors: Efstratios Karavasilis, Foteini Christidi, Georgios Velonakis, Agapi Plousi, Kalliopi Platoni, Nikolaos Kelekis, Ioannis Evdokimidis, Efstathios Efstathopoulos

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Introduction: Advanced structural and functional neuroimaging techniques contribute to the study of anatomical and functional brain connectivity and its role in the pathophysiology and symptoms’ heterogeneity in several neurodegenerative disorders, including multiple sclerosis (MS). Aim: In the present study, we applied multiparametric neuroimaging techniques to investigate the structural and functional cortico-cerebellar changes in MS patients. Material: We included 51 MS patients (28 with clinically isolated syndrome [CIS], 31 with relapsing-remitting MS [RRMS]) and 51 age- and gender-matched healthy controls (HC) who underwent MRI in a 3.0T MRI scanner. Methodology: The acquisition protocol included high-resolution 3D T1 weighted, diffusion-weighted imaging and echo planar imaging sequences for the analysis of volumetric, tractography and functional resting state data, respectively. We performed between-group comparisons (CIS, RRMS, HC) using CAT12 and CONN16 MATLAB toolboxes for the analysis of volumetric (cerebellar gray matter density) and functional (cortico-cerebellar resting-state functional connectivity) data, respectively. Brainance suite was used for the analysis of tractography data (cortico-cerebellar white matter integrity; fractional anisotropy [FA]; axial and radial diffusivity [AD; RD]) to reconstruct the cerebellum tracts. Results: Patients with CIS did not show significant gray matter (GM) density differences compared with HC. However, they showed decreased FA and increased diffusivity measures in cortico-cerebellar tracts, and increased cortico-cerebellar functional connectivity. Patients with RRMS showed decreased GM density in cerebellar regions, decreased FA and increased diffusivity measures in cortico-cerebellar WM tracts, as well as a pattern of increased and mostly decreased functional cortico-cerebellar connectivity compared to HC. The comparison between CIS and RRMS patients revealed significant GM density difference, reduced FA and increased diffusivity measures in WM cortico-cerebellar tracts and increased/decreased functional connectivity. The identification of decreased WM integrity and increased functional cortico-cerebellar connectivity without GM changes in CIS and the pattern of decreased GM density decreased WM integrity and mostly decreased functional connectivity in RRMS patients emphasizes the role of compensatory mechanisms in early disease stages and the disintegration of structural and functional networks with disease progression. Conclusions: In conclusion, our study highlights the added value of multimodal neuroimaging techniques for the in vivo investigation of cortico-cerebellar brain changes in neurodegenerative disorders. An extension and future opportunity to leverage multimodal neuroimaging data inevitably remain the integration of such data in the recently-applied mathematical approaches of machine learning algorithms to more accurately classify and predict patients’ disease course.

Keywords: advanced neuroimaging techniques, cerebellum, MRI, multiple sclerosis

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Authors: Yasir Ali Soomro

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Companies that demonstrate corporate social responsibilities (CSR) are more likely to withstand any downturn or crises because of the trust built with stakeholders. Many firms are utilizing CSR marketing to improve the interactions with their various stakeholders, mainly the consumers. Most previous research on CSR has focused on the impact of CSR on customer responses and behaviors toward a company. As online food ordering and grocery shopping remains inevitable. This study will investigate structural relationships among consumer positive emotions (CPE) and negative emotions (CNE), Corporate Reputation (CR), Customer Trust (CT), Brand Image (BI), and Brand attitude (BA) on behavioral outcomes such as Online purchase intention (OPI) and Word of mouth (WOM) in retail grocery and food restaurants setting. Hierarchy of Effects Model will be used as theoretical, conceptual framework. The model describes three stages of consumer behavior: (i) cognitive, (ii) affective, and (iii) conative. The study will apply a quantitative method to test the hypotheses; a self-developed questionnaire with non-probability sampling will be utilized to collect data from 500 consumers belonging to generation X, Y, and Z residing in KSA. The study will contribute by providing empirical evidence to support the link between CSR and customer affective and conative experiences in Saudi Arabia. The theoretical contribution of this study will be empirically tested comprehensive model where CPE, CNE, CR, CT, BI, and BA act as mediating variables between the perceived CSR & Online purchase intention (OPI) and Word of mouth (WOM). Further, the study will add more to how the emotional/ psychological process mediates in the CSR literature, especially in the Middle Eastern context. The proposed study will also explain the effect of perceived CSR marketing initiatives directly and indirectly on customer behavioral responses.

Keywords: corporate social responsibility, corporate reputation, consumer emotions, loyalty, online purchase intention, word-of-mouth, structural equation modeling

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Authors: Naser Sabaghnia, Mohsen Janmohammadi, Mehdi Mohebodini

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Present investigation is performed to evaluate the effects of foliar application of salicylic acid, glycine betaine, ascorbic acid, nano-silica, and nano-titanium dioxide on sunflower. Results showed that the first two principal components were sufficient to create a two-dimensional treatment by trait biplot, and such biplot accounted percentages of 49% and 19%, respectively of the interaction between traits and treatments. The vertex treatments of polygon were ascorbic acid, glycine betaine, nano-TiO₂, and control indicated that high performance in some important traits consists of number of days to seed maturity, number of seeds per head, number heads per single plant, hundred seed weight, seed length, seed yield performance, and oil content. Treatments suitable for obtaining the high seed yield were identified in the vector-view function of biplot and displayed nano-silica and nano titanium dioxide as the best treatments suitable for obtaining of high seed yield.

Keywords: drought stress, nano-silicon dioxide, oil content, TiO2 nanoparticles

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