Search results for: random fields

Authors: Shamm Kidane, Ibrahim Abdella, Fitsum Gaim, Simon Mulugeta, Sirak Asmerom, Natnael Ambasager, Yoel Ghebrihiwot

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The call for natural language processing (NLP) systems for low-resource languages has become more apparent than ever in the past few years, with the arduous challenges still present in preparing such systems. This paper presents an improved dataset version of the Nagaoka Tigrinya Corpus for Parts-of-Speech (POS) classification system in the Tigrinya language. The size of the initial Nagaoka dataset was incremented, totaling the new tagged corpus to 118K tokens, which comprised the 12 basic POS annotations used previously. The additional content was also annotated manually in a stringent manner, followed similar rules to the former dataset and was formatted in CONLL format. The system made use of the novel approach in NLP tasks and use of the monolingually pre-trained TiELECTRA, TiBERT and TiRoBERTa transformer models. The highest achieved score is an impressive weighted F1-score of 94.2%, which surpassed the previous systems by a significant measure. The system will prove useful in the progress of NLP-related tasks for Tigrinya and similarly related low-resource languages with room for cross-referencing higher-resource languages.

Keywords: Tigrinya POS corpus, TiBERT, TiRoBERTa, conditional random fields

Procedia PDF Downloads 104

Authors: Hui Wang

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Traditional collection building approaches are limited in breadth and scope and are not necessarily suitable for multidisciplinary fields development in the institutes of the Chinese Academy of Sciences. The increasing of multidisciplinary fields researches require a viable approach to collection development in these libraries. This study uses qualitative and quantitative analysis to assess collection. The quantitative analysis consists of three levels of evaluation, which including realistic demand, potential demand and trend demand analysis. For one institute, three samples were separately selected from the object institute, more than one international top institutes in highly relative research fields and future research hotspots. Each sample contains an appropriate number of papers published in recent five years. Several keywords and the organization names were reasonably combined to search in commercial databases and the institutional repositories. The publishing information and citations in the bibliographies of these papers were selected to build the dataset. One weighted evaluation model and citation analysis were used to calculate the demand intensity index of every journal and book. Principal Investigator selector and database traffic provide a qualitative evidence to describe the demand frequency. The demand intensity, demand frequency and academic committee recommendations were comprehensively considered to recommend collection development. The collection gaps or weaknesses were ascertained by comparing the current collection and the recommend collection. This approach was applied in more than 80 institutes’ libraries in Chinese Academy of Sciences in the past three years. The evaluation results provided an important evidence for collections building in the second year. The latest user survey results showed that the updated collection’s capacity to support research in a multidisciplinary subject area have increased significantly.

Keywords: citation analysis, collection assessment, collection development, quantitative analysis

Procedia PDF Downloads 219

Authors: Aleš Florian, Lenka Ševelová

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Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.

Keywords: concrete, FEM, pavement, sensitivity, simulation

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Authors: A. A. Hussen, I. N. Jleta

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Micro-electromechanical system (MEMS) accelerometers and gyroscopes are suitable for the inertial navigation system (INS) of many applications due to the low price, small dimensions and light weight. The main disadvantage in a comparison with classic sensors is a worse long term stability. The estimation accuracy is mostly affected by the time-dependent growth of inertial sensor errors, especially the stochastic errors. In order to eliminate negative effect of these random errors, they must be accurately modeled. Where the key is the successful implementation that depends on how well the noise statistics of the inertial sensors is selected. In this paper, the Allan variance technique will be used in modeling the stochastic errors of the inertial sensors. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data.

Keywords: Allan variance, accelerometer, gyroscope, stochastic errors

Procedia PDF Downloads 442

Authors: Habibollah Mashhady, Behruz Lotfi, Mohammad Doosti, Moslem Fatollahi

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An exploration of the related literature reveals that all instruction methods aim at training autonomous learners. After the turn of second language pedagogy toward learner-oriented strategies, learners’ needs were more focused. Yet; the historical, social and political aspects of learning were still neglected. The present study investigates the notion of autonomous learning and explains its various facets from a pedagogical point of view. Furthermore; different elements, fields and scopes of autonomous learning will be explored. After exploring different aspects of autonomy, it is postulated that liberatory autonomy is highlighted since it not only covers social autonomy but also reveals learners’ capabilities and human potentials. It is also recommended that learners consider different elements of autonomy such as motivation, knowledge, confidence, and skills.

Keywords: critical pedagogy, social autonomy, academic learning, cultural notions

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Authors: Khobaib Khobaib, Alexander Mikkelsen, Zbigniew Rozynek

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Electric fields have proven useful for inducing droplet deformation and to structure particles adsorbed at droplet interfaces. In this experimental research, direct current electric fields were applied to deform particle-covered droplets made out of silicone oil and immersed in castor oil. The viscosity of the drop and surrounding fluid were changed by external heating. We designed an experimental system in such a way that electric field-induced electrohydrodynamic (EHD) flows were asymmetric and only present on one side of the drop, i.e., the droplet adjoined a washer and adhered to one of the electrodes constituting the sample cell. The study investigated the influence of viscosity on the steady-state deformation magnitude of particle-laden droplets, droplet compression, and relaxation, as well as particle arrangements at drop interfaces. Initially, before the application of an electric field, we changed the viscosity of the fluids by heating the sample cell at different temperatures. The viscosity of the fluids was varied by changing the temperature of the fluids from 25 to 50°C. Under the application of a uniform electric field of strength 290 Vmm⁻¹, electric stress was induced at the drop interface, yielding drop deformation. In our study, we found that by lowering the fluid viscosity, the velocity of the EHD flows was increased, which also increases the deformation of the drop.

Keywords: drop deformation and relaxation, electric field, electrohydrodynamic flow, particle assembly, viscosity

Procedia PDF Downloads 267

Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: document analysis, sentimental analysis, emotion detection, WEKA tool, NRC lexicon

Procedia PDF Downloads 444

Authors: Nazakat Ismailzada, Carol Jones

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Tomato drilling moth Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is the main pest of tomato plants in many countries. The larvae of tomato leaves, the stems inside, in the end buds, they opened the gallery in green and ripe fruit. In this way the harmful products can be fed with all parts of the tomato plant can cause damage to 80-100%. Pest harms all above ground parts of the tomato plant. After the seedlings are planted in areas and during blossoming holder traps with tomato moth’s rubber capsule inside should be placed in the area by using five-tomato moth’s feremon per ha. Then there should be carried out observations in the fields in every three days regularly. During the researches, it was showed that in field condition Carogen 20 SC besides high-level biological efficiency also has low ecological load for environment, and should be used against tomato moth in farms. Therefore it was showed that in field condition Carogen 20 SC besides high-level biological efficiency also has low ecological load for environment, and should be used against tomato moth in farms with insecticide expenditure norm 320 qr\ha. In farms should be used plant rotation, plant fields should be plowed on the 25-30 sm depth, before sowing seeds should be proceeded by insecticides. As element of integrated plant protection activities, should be used pheromones trap. In tomato plant fields as an insecticide should be used AGROSAN 240 SC and Carogen 20 SP.

Keywords: lepidoptera, Tuta absoluta, chemical control, integrated pest management

Procedia PDF Downloads 167

Authors: Shabnam Najafi, Metin Turkay

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Network design problem (NDP) is used to determine the set of optimal values for certain pre-specified decision variables such as capacity expansion of nodes and links by optimizing various system performance measures including safety, congestion, and accessibility. The designed transportation network should improve objective functions defined for the system by considering the route choice behaviors of network users at the same time. The NDP studies mostly investigated the random demand and route selection constraints separately due to computational challenges. In this work, we consider both random demand and route selection constraints simultaneously. This work presents a nonlinear stochastic model for land use and road network design problem to address the development of different functional zones in urban areas by considering both cost function and air pollution. This model minimizes cost function and air pollution simultaneously with random demand and stochastic route selection constraint that aims to optimize network performance via road capacity expansion. The Bureau of Public Roads (BPR) link impedance function is used to determine the travel time function in each link. We consider a city with origin and destination nodes which can be residential or employment or both. There are set of existing paths between origin-destination (O-D) pairs. Case of increasing employed population is analyzed to determine amount of roads and origin zones simultaneously. Minimizing travel and expansion cost of routes and origin zones in one side and minimizing CO emission in the other side is considered in this analysis at the same time. In this work demand between O-D pairs is random and also the network flow pattern is subject to stochastic user equilibrium, specifically logit route choice model. Considering both demand and route choice, random is more applicable to design urban network programs. Epsilon-constraint is one of the methods to solve both linear and nonlinear multi-objective problems. In this work epsilon-constraint method is used to solve the problem. The problem was solved by keeping first objective (cost function) as the objective function of the problem and second objective as a constraint that should be less than an epsilon, where epsilon is an upper bound of the emission function. The value of epsilon should change from the worst to the best value of the emission function to generate the family of solutions representing Pareto set. A numerical example with 2 origin zones and 2 destination zones and 7 links is solved by GAMS and the set of Pareto points is obtained. There are 15 efficient solutions. According to these solutions as cost function value increases, emission function value decreases and vice versa.

Keywords: epsilon-constraint, multi-objective, network design, stochastic

Procedia PDF Downloads 648

Authors: Felicity Aphiwe Mkhongi, Walter Musakwa

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Smallholder crop production is a defining factor in achieving food security, particularly at the household level. However, the number of abandoned arable fields is increasing in communal areas of South Africa. While substantial efforts have been devoted to addressing food insecurity in the country, ownership of arable land has not been supplemented with sustainable food production for households. This paper analyses household food security in the context of deagrarianization in two villages, Ambros (Eastern Cape) and Maramanzhi (Limpopo). Semi-structured questionnaires were administered to acquire both qualitative and quantitative data from 106 heads of households. The IBM SPSS Statistics 28.0 computer program was applied to complete data analysis. From the findings of the study, it was evident that compared to arable fields, a greater proportion of households own home-gardens with an average size of 2100m in Ambros and 3400m in Maramanzhi village. The majority of arable fields were abandoned, particularly in Ambros village. Household food access challenges were measured using the Household Food Insecurity Access Scale (HFIAS). This food security indicator revealed that the majority of households were mildly food insecure owing to food shortages emanating from insufficient monthly income and waning household crop production. Food was rated as a very important reason for engaging in cultivation in both villages of the study, but deagrarianization has eroded opportunities for increasing household crop production. Among other possible solutions, this study recommends that the government invest more in agriculture to allow for sustainable strategies that revive abandoned arable land, such as arable fields in communal areas of South Africa, as this could increase food production for households.

Keywords: cultivation, deagrarianization, food security, rural households, smallholder farmers

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Authors: Wenjie Qin, Kewei Lv

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In 1996, Boneh and Venkatesan proposed the Hidden Number Problem (HNP) and proved the most significant bits (MSB) of computational Diffie-Hellman key exchange scheme and related schemes are unpredictable bits. They also gave a method which is a lattice rounding technique to solve HNP in non-uniform model. In this paper, we put forward a new concept that is Schnorr-MSB-HNP. We also reduce the problem of solving Schnorr signature private key with a few consecutive most significant bits of random nonce (used at each signature generation) to Schnorr-MSB-HNP, then we use the rounding technique to solve the Schnorr-MSB-HNP. We have come to the conclusion that if there is a ‘miraculous box’ which inputs the random nonce and outputs 2loglogq (q is a prime number) most significant bits of nonce, the signature private key will be obtained by choosing 2logq signature messages randomly. Thus we get an attack on the Schnorr signature private key.

Keywords: rounding technique, most significant bits, Schnorr signature algorithm, nonce, Schnorr-MSB-HNP

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Kailash C. Madan

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We study the steady state behaviour of a batch arrival single server queue in which the first service with general service times is compulsory and the second service with general service times is optional. We term such a two phase service as generalized Coxian-2 service. Just after completion of a service the server must take a vacation of random length of time with general vacation times. We obtain steady state probability generating functions for the queue size as well as the steady state mean queue size at a random epoch of time in explicit and closed forms. Some particular cases of interest including some known results have been derived.

Keywords: batch arrivals, compound Poisson process, generalized Coxian-2 service, steady state

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Authors: Yaping Zhao, Yimin Zhang

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In the present paper, the stationary random vibration of a uniform cantilever beam is investigated. Two types of damping mechanism, i.e. the external and internal viscous dampings, are taken into account simultaneously. The excitation form is the support motion, and it is ideal white. Because two type of damping mechanism are considered concurrently, the product of the modal damping ratio and the natural frequency is not a constant anymore. As a result, the infinite definite integral encountered in the process of computing the mean square response is more complex than that in the existing literature. One signal progress of this work is to have calculated these definite integrals accurately. The precise solution of the mean square response is thus obtained in the infinite series form finally. Numerical examples are supplied and the numerical outcomes acquired confirm the validity of the theoretical analyses.

Keywords: random vibration, cantilever beam, mean square response, white noise

Procedia PDF Downloads 384

Authors: Aya Salama

Abstract:

Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

Procedia PDF Downloads 89

Authors: S. Joseph Antony, Z. K. Jahanger

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Soils are subjected to cyclic loading in situ in situations such as during earthquakes and in the compaction of pavements. Investigations on the local scale measurement of the displacements of the grain and failure patterns within the soil bed under the cyclic loading conditions are rather limited. In this paper, using the digital particle image velocimetry (DPIV), local scale displacement fields of a dense sand medium interacting with a rigid footing are measured under the plane-strain condition for two commonly used types of cyclic loading, and the quasi-static loading condition for the purposes of comparison. From the displacement measurements of the grains, the failure envelopes of the sand media are also presented. The results show that, the ultimate cyclic bearing capacity (qultcyc) occurred corresponding to a relatively higher settlement value when compared with that of under the quasi-static loading. For the sand media under the cyclic loading conditions considered here, the displacement fields in the soil media occurred more widely in the horizontal direction and less deeper along the vertical direction when compared with that of under the quasi-static loading. The 'dead zone' in the sand grains beneath the footing is identified for all types of the loading conditions studied here. These grain-scale characteristics have implications on the resulting bulk bearing capacity of the sand media in footing-sand interaction problems.

Keywords: cyclic loading, DPIV, settlement, soil-structure interactions, strip footing

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Authors: Sajjad Asefi, Hossein Afrakhte

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This paper presents the evaluation of reliability indices of an electrical distribution system using Monte Carlo simulation technique accounting Time To Switch (TTS) for each section. In this paper, the distribution system has been assumed by accounting random repair time omission. For simplicity, we have assumed the reliability analysis to be based on exponential law. Each segment has a specified rate of failure (λ) and repair time (r) which will give us the mean up time and mean down time of each section in distribution system. After calculating the modified mean up time (MUT) in years, mean down time (MDT) in hours and unavailability (U) in h/year, TTS have been added to the time which the system is not available, i.e. MDT. In this paper, we have assumed the TTS to be a random variable with Log-Normal distribution.

Keywords: distribution system, Monte Carlo simulation, reliability, repair time, time to switch (TTS)

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

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Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, Unmanned Aerial Vehicle (UAV), uniform, random, kriging

Procedia PDF Downloads 158

Authors: Muhammad Ahsan

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The effective decommissioning of oil and gas fields and related assets is one of the most important challenges facing the oil and gas industry today and in the future. Decommissioning decisions can no longer be avoided by the operators and the industry as a whole. Decommissioning yields no return on investment and carries significant regulatory liabilities. The main objective of this paper is to provide an approach and mechanism for the estimation of emissions associated with decommissioning of Oil and Gas fields. The model uses gate to gate approach and considers field life from development phase up to asset end life. The model incorporates decommissioning processes which includes; well plugging, plant dismantling, wellhead, and pipeline dismantling, cutting and temporary fabrication, new manufacturing from raw material and recycling of metals. The results of the GHG emissions during decommissioning phase are 2.31x10-2 Kg CO2 Eq. per Mcf of the produced natural gas. Well plug and abandonment evolved to be the most GHG emitting activity with 84.7% of total field decommissioning operational emissions.

Keywords: LCA (life cycle analysis), gas field, decommissioning, emissions

Procedia PDF Downloads 187

Authors: Thomas Chinwe Urama, Patrick Oseloka Ezepue, Peters Chimezie Nnanwa

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This paper investigates the universal financial dynamics in two dominant stock markets in Sub-Saharan Africa, through an in-depth analysis of the cross-correlation matrix of price returns in Nigerian Stock Market (NSM) and Johannesburg Stock Exchange (JSE), for the period 2009 to 2013. The strength of correlations between stocks is known to be higher in JSE than that of the NSM. Particularly important for modelling Nigerian derivatives in the future, the interactions of other stocks with the oil sector are weak, whereas the banking sector has strong positive interactions with the other sectors in the stock exchange. For the JSE, it is the oil sector and beverages that have greater sectorial correlations, instead of the banks which have the weaker correlation with other sectors in the stock exchange.

Keywords: random matrix theory, cross-correlations, emerging markets, option pricing, eigenvalues eigenvectors, inverse participation ratios and implied volatility

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: CarlyWill Sloan

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Racial disparities in criminal justice outcomes are well-documented. However, there is little evidence on the extent to which racial bias by prosecutors is responsible for these disparities. This paper tests for racial bias in conviction by prosecutors. To identify effects, this paper leverages as good as random variation in prosecutor race using detailed administrative data on the case assignment process and case outcomes in New York County, New York. This paper shows that the assignment of an opposite-race prosecutor leads to a 5 percentage point (~ 8 percent) increase in the likelihood of conviction for property crimes. There is no evidence of effects for other types of crimes. Additional results indicate decreased dismissals by opposite-race prosecutors likely drive my property crime estimates.

Keywords: criminal justice, discrimination, prosecutors, racial disparities

Procedia PDF Downloads 191

Authors: Ömer Aktaş, Olga A. Suvorova, Oleg Tretyakov

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The boundary value problem on non-canonical and arbitrary shaped contour is solved with a numerically effective method called Analytical Regularization Method (ARM) to calculate propagation parameters. As a result of regularization, the equation of first kind is reduced to the infinite system of the linear algebraic equations of the second kind in the space of L2. This equation can be solved numerically for desired accuracy by using truncation method. The parameters as cut-off wavenumber and cut-off frequency are used in waveguide evolutionary equations of electromagnetic theory in time-domain to illustrate the real-valued TM fields with lossy and lossless media.

Keywords: analytical regularization method, electromagnetic theory evolutionary equations of time-domain, TM Field

Procedia PDF Downloads 501

Authors: Po-Wen Chen, Jin-Yu Wu, Md. Manirul Ali, Yang Peng, Chen-Te Chang, Der-Jun Jan

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Dynamics of cathode spot has become a major part of vacuum arc discharge with its high academic interest and wide application potential. In this article, using a three-dimensional statistical model, we simulate the distribution of the ignition probability of a new cathode spot occurring in different magnetic pressure on old cathode spot surface and at different arcing time. This model for the ignition probability of a new cathode spot was proposed in two typical situations, one by the pure isotropic random walk in the absence of an external magnetic field, other by the retrograde motion in external magnetic field, in parallel with the cathode surface. We mainly focus on developed relationship between the ignition probability density distribution of a new cathode spot and the external magnetic field.

Keywords: cathode spot, vacuum arc discharge, transverse magnetic field, random walk

Procedia PDF Downloads 434

Authors: Zhang Zijian

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Nowadays, spherical robots have played an important role in many fields, but the insufficient ability of obstacle surmounting limits their wider application fields. To solve this problem, a jumping system of a spherical robot is designed based on Archimedes helix. The jumping system of the robot utilizes the characteristics of Archimedes helix and isovelocity helix to achieve constant speed and stable contraction, which ensures the stability of the system. Also, the jumping action of the robot is realized by instantaneous release of elastic potential energy. In order to verify the effectiveness of the jumping system, we designed a spherical robot and its jumping system. The experimental results show that the jumping system has the advantages of light weight, small size, high energy conversion efficiency, and can realize the spherical jumping function.

Keywords: hopping mechanism, Archimedes' Helix, hopping robot, spherical robot

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Authors: Tomasz Borowski, Dawid Sołoducha, Rafał Rakoczy, Marian Kordas

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The application of magnetic field is essential for a wide range of technologies or processes (i.e., magnetic hyperthermia, bioprocessing). From the practical point of view, bioprocess control is often limited to the regulation of temperature at constant values favourable to microbial growth. The main aim of this study is to determine the effect of various types of electromagnetic fields (i.e., static or alternating) on the heat transfer in a self-designed magnetically assisted reactor. The experimental set-up is equipped with a measuring instrument which controlled the temperature of the liquid inside the container and supervised the real-time acquisition of all the experimental data coming from the sensors. Temperature signals are also sampled from generator of magnetic field. The obtained temperature profiles were mathematically described and analyzed. The parameters characterizing the response to a step input of a first-order dynamic system were obtained and discussed. For example, the higher values of the time constant means slow signal (in this case, temperature) increase. After the period equal to about five-time constants, the sample temperature nearly reached the asymptotic value. This dynamical analysis allowed us to understand the heating effect under the action of various types of electromagnetic fields. Moreover, the proposed mathematical description can be used to compare the influence of different types of magnetic fields on heat transfer operations.

Keywords: heat transfer, magnetically assisted reactor, dynamical analysis, transient function

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Authors: Mohamed Abdel Sabour, Mohamed Nouh, Ian Stevans, Essam Elkholy

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We study the period change of six Cepheids using 19376 accurate flux observations of the Solar Mass Ejection Imager (SMEI) onboard the Coriolis spacecraft. All observations for the six Cepheids have been derived as templates for each star, independent of the specific sites utilized to establish and update the O-C values. Sometimes, sinusoidal patterns are superimposed on the star's O-C changes, which cannot be regarded as random fluctuations in the pulsation period. Random period changes were detected and computed using Eddington's and Plakidis's approaches. A comparison of the observed and predicted period change reveals a good agreement with some published models and a very substantial divergence with others. Between the reported period change and that estimated by the current technique, a linear fit with a correlation coefficient of 90.08 percent was obtained. The temporal rate of period change in Cepheid stars might be connected to how well these stars' mass losses are known today.

Keywords: cepheids, period change, mass loss, O-C changes, period change, mass loss, O-C

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Authors: Valery A. Aurov, Maria D. Bausheva, Elena V. Uliyanova

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The problems of sustainability of contemporary high-rise buildings now demand an altogether new approach, which corresponds with the laws of dialectics. We should imply the principle “going from mega-object to the so called mezzo-object.” So the scientists have arrived at the conclusion that a contemporary “skyscraper” must not increase in height but develop horizontal space axes which unite a complex of high-rise buildings into a single composition. This is necessary both for safety issues and increasing skyscrapers’ functioning qualities. As a result, architects single out a quality unit in a dominating group of high-rise constructions and make a conclusion about the influence of visual fields on the designing parameters of this group.

Keywords: design, high-rise buildings, skyscrapers, sustainability, visual fields, dominating group, regulations, design recommendations

Procedia PDF Downloads 373

Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su, Junjie Wang

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The Bernoulli filter is a precise Bayesian filter for single target tracking based on the random finite set theory. The standard Bernoulli filter often underestimates the number of targets. This study proposes a Gaussian particle flow (GPF) Bernoulli filter employing particle flow to migrate particles from prior to posterior positions to improve the performance of the standard Bernoulli filter. By employing the particle flow filter, the computational speed of the Bernoulli filters is significantly improved. In addition, the GPF Bernoulli filter provides a more accurate estimation compared with that of the standard Bernoulli filter. Simulation results confirm the improved tracking performance and computational speed in two- and three-dimensional scenarios compared with other algorithms.

Keywords: Bernoulli filter, particle filter, particle flow filter, random finite sets, target tracking

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Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

Abstract:

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

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