Search results for: semiparametric transformation models

Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Suparman Suparman

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A white noise in autoregressive (AR) model is often assumed to be normally distributed. In application, the white noise usually do not follows a normal distribution. This paper aims to estimate a parameter of AR model that has a exponential white noise. A Bayesian method is adopted. A prior distribution of the parameter of AR model is selected and then this prior distribution is combined with a likelihood function of data to get a posterior distribution. Based on this posterior distribution, a Bayesian estimator for the parameter of AR model is estimated. Because the order of AR model is considered a parameter, this Bayesian estimator cannot be explicitly calculated. To resolve this problem, a method of reversible jump Markov Chain Monte Carlo (MCMC) is adopted. A result is a estimation of the parameter AR model can be simultaneously calculated.

Keywords: autoregressive (AR) model, exponential white Noise, bayesian, reversible jump Markov Chain Monte Carlo (MCMC)

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Authors: X. Li, Y. Liu, H. Wong, B. Pardoen, A. Fabbri, F. McGregor, E. Liu

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The target of this paper is to investigate how saturated poroelastic soils subject to freezing temperatures behave and how different boundary conditions can intervene and affect the thermo-hydro-mechanical (THM) responses, based on a particular but classical configuration of a finite homogeneous soil layer studied by Terzaghi. The essential relations on the constitutive behavior of a freezing soil are firstly recalled: ice crystal - liquid water thermodynamic equilibrium, hydromechanical constitutive equations, momentum balance, water mass balance, and the thermal diffusion equation, in general, non-linear case where material parameters are state-dependent. The system of equations is firstly linearized, assuming all material parameters to be constants, particularly the permeability of liquid water, which should depend on the ice content. Two analytical solutions solved by the classic Laplace transform are then developed, accounting for two different sets of boundary conditions. Afterward, the general non-linear equations with state-dependent parameters are solved using a commercial code COMSOL based on finite elements method to obtain numerical results. The validity of this numerical modeling is partially verified using the analytical solution in the limiting case of state-independent parameters. Comparison between the results given by the linearized analytical solutions and the non-linear numerical model reveals that the above-mentioned linear computation will always underestimate the liquid pore pressure and displacement, whatever the hydraulic boundary conditions are. In the nonlinear model, the faster growth of ice crystals, accompanying the subsequent reduction of permeability of freezing soil layer, makes a longer duration for the depressurization of water liquid and slower settlement in the case where the ground surface is swiftly covered by a thin layer of ice, as well as a bigger global liquid pressure and swelling in the case of the impermeable ground surface. Nonetheless, the analytical solutions based on linearized equations give a correct order-of-magnitude estimate, especially at moderate temperature variations, and remain a useful tool for preliminary design checks.

Keywords: chemical potential, cryosuction, Laplace transform, multiphysics coupling, phase transformation, thermodynamic equilibrium

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Authors: Aura Alejandra Burbano Patino, Mariela Agotegaray, Veronica Lassalle, Fernanda Horst

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Water pollution is of worldwide concern due to its importance as an essential resource for life. Industrial and urbanistic growth are anthropogenic activities that have caused an increase of undesirable compounds in water. In the last decade, emerging pollutants have become of great interest since, at very low concentrations (µg/L and ng/L), they exhibit a hazardous effect on wildlife, aquatic ecosystems, and human organisms. One group of emerging pollutants that are a matter of study are pharmaceuticals. Their high consumption rate and their inappropriate disposal have led to their detection in wastewater treatment plant influent, effluent, surface water, and drinking water. In consequence, numerous technologies have been developed to efficiently treat these pollutants. Adsorption appears like an easy and cost-effective technology. One of the most used adsorbents of emerging pollutants removal is carbon-based materials such as hydrochars. This study aims to use a magnetic hydrochar nanocomposite to be employed as an adsorbent for diclofenac removal. Kinetics models and the adsorption efficiency in real water samples were analyzed. For this purpose, a magnetic hydrochar nanocomposite was synthesized through the hydrothermal carbonization (HTC) technique hybridized to co-precipitation to add the magnetic component into the hydrochar, based on iron oxide nanoparticles. The hydrochar was obtained from sunflower husk residue as the precursor. TEM, TGA, FTIR, Zeta potential as a function of pH, DLS, BET technique, and elemental analysis were employed to characterize the material in terms of composition and chemical structure. Adsorption kinetics were carried out in distilled water and real water at room temperature, pH of 5.5 for distilled water and natural pH for real water samples, 1:1 adsorbent: adsorbate dosage ratio, contact times from 10-120 minutes, and 50% dosage concentration of DCF. Results have demonstrated that magnetic hydrochar presents superparamagnetic properties with a saturation magnetization value of 55.28 emu/g. Besides, it is mesoporous with a surface area of 55.52 m²/g. It is composed of magnetite nanoparticles incorporated into the hydrochar matrix, as can be proven by TEM micrographs, FTIR spectra, and zeta potential. On the other hand, kinetic studies were carried out using DCF models, finding percent removal efficiencies up to 85.34% after 80 minutes of contact time. In addition, after 120 minutes of contact time, desorption of emerging pollutants from active sites took place, which indicated that the material got saturated after that t time. In real water samples, percent removal efficiencies decrease up to 57.39%, ascribable to a possible mechanism of competitive adsorption of organic or inorganic compounds, ions for active sites of the magnetic hydrochar. The main suggested adsorption mechanism between the magnetic hydrochar and diclofenac include hydrophobic and electrostatic interactions as well as hydrogen bonds. It can be concluded that the magnetic hydrochar nanocomposite could be valorized into a by-product which appears as an efficient adsorbent for DCF removal as a model emerging pollutant. These results are being complemented by modifying experimental variables such as pollutant’s initial concentration, adsorbent: adsorbate dosage ratio, and temperature. Currently, adsorption assays of other emerging pollutants are being been carried out.

Keywords: environmental remediation, emerging pollutants, hydrochar, magnetite nanoparticles

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Authors: Carolina Cuéllar, Paz González

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Research has shown that school leadership has a significant indirect effect on students’ achievements. In Chile, evidence has also revealed that this impact is stronger in vulnerable schools. With the aim of strengthening school leadership, public policy has taken up the challenge of enhancing capabilities of novice principals through the implementation of induction programs, which include a mentoring component, entrusting the task of delivering these programs to universities. The importance of using mentoring or coaching models in the preparation of novice school leaders has been emphasized in the international literature. Thus, it can be affirmed that building leadership capacity through partnership is crucial to facilitate cognitive and affective support required in the initial phase of the principal career, gain role clarification and socialization in context, stimulate reflective leadership practice, among others. In Chile, mentoring is a recent phenomenon in the field of school leadership and it is even more new in the preparation of new principals who work in public schools. This study, funded by the Chilean Ministry of Education, sought to explore the challenges and lessons arising from the design and implementation of mentoring processes which are part of the induction programs, according to the perception of the different actors involved: ministerial agents, university coordinators, mentors and novice principals. The investigation used a qualitative design, based on a study of three cases (three induction programs). The sources of information were 46 semi-structured interviews, applied in two moments (at the beginning and end of mentoring). Content analysis technique was employed. Data focused on the uniqueness of each case and the commonalities within the cases. Five main challenges and lessons emerged in the design and implementation of mentoring within the induction programs for new principals from Chilean public schools. They comprised the need of (i) developing a shared conceptual framework on mentoring among the institutions and actors involved, which helps align the expectations for the mentoring component within the induction programs, along with assisting in establishing a theory of action of mentoring that is relevant to the public school context; (ii) recognizing trough actions and decisions at different levels that the role of a mentor differs from the role of a principal, which challenge the idea that an effective principal will always be an effective mentor; iii) improving mentors’ selection and preparation processes trough the definition of common guiding criteria to ensure that a mentor takes responsibility for developing critical judgment of novice principals, which implies not limiting the mentor’s actions to assist in the compliance of prescriptive practices and standards; (iv) generating common evaluative models with goals, instruments and indicators consistent with the characteristics of mentoring processes, which helps to assess expected results and impact; and (v) including the design of a mentoring structure as an outcome of the induction programs, which helps sustain mentoring within schools as a collective professional development practice. Results showcased interwoven elements that entail continuous negotiations at different levels. Taking action will contribute to policy efforts aimed at professionalizing the leadership role in public schools.

Keywords: induction programs, mentoring, novice principals, school leadership preparation

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Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Piyush Swami, Bijaya Ketan Panigrahi, Sneh Anand, Manvir Bhatia, Tapan Gandhi

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Visual inspection of electroencephalogram (EEG) signals to detect epileptic signals is very challenging and time-consuming task even for any expert neurophysiologist. This problem is most challenging in under-developed and developing countries due to shortage of skilled neurophysiologists. In the past, notable research efforts have gone in trying to automate the seizure detection process. However, due to high false alarm detections and complexity of the models developed so far, have vastly delimited their practical implementation. In this paper, we present a novel scheme for epileptic seizure detection using empirical mode decomposition technique. The intrinsic mode functions obtained were then used to calculate the standard deviations. This was followed by probability density based classifier to discriminate between non-ictal and ictal patterns in EEG signals. The model presented here demonstrated very high classification rates ( > 97%) without compromising the statistical performance. The computation timings for each testing phase were also very low ( < 0.029 s) which makes this model ideal for practical applications.

Keywords: electroencephalogram (EEG), epilepsy, ictal patterns, empirical mode decomposition

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Authors: Manasse Elusma, Thung-Hong Lin, Chun-yin Lee

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The relationship between hunger and war or conflict remains to be discussed. Do wars or conflicts cause hunger and food scarcity, or is the reverse relationship is true? As the world becomes more peaceful and wealthier, some countries are still suffered from hunger and food shortage. So, eradicating hunger calls for a more comprehensive understanding of the relationship between conflict and hunger. Several studies are carried out to detect the importance of conflict or war on food security. Most of these studies, however, perform only descriptive analysis and largely use food security indicators instead of the global hunger index. Few studies have employed cross-country panel data to explicitly analyze the association between conflict and chronic hunger, including hidden hunger. Herein, this study addresses this issue and the knowledge gap. We combine global datasets to build a new panel dataset including 143 countries from 1989 to 2020. This study examines the effect of conflict on hunger with fixed effect models, and the results show that the increase of conflict frequency deteriorates hunger. Peacebuilding efforts and war prevention initiative are required to eradicate global hunger.

Keywords: armed conflict, food scarcity, hidden hunger, hunger, malnutrition

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Authors: A. Shafaghat, H. Khabbaz, S. Moravej, Ah. Shafaghat

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The bearing capacity of closely spaced shallow footings alters with their spacing and the shape of footing. In this study, the bearing capacity and settlement of two adjacent footings constructed on a sand layer are investigated. The effect of different footing shapes including square, circular, ring and strip on sandy soil is captured in the calculations. The investigations are carried out numerically using PLAXIS-3D software and analytically employing conventional settlement equations. For this purpose, foundations are modelled in the program with practical dimensions and various spacing ratios ranging from 1 to 5. The spacing ratio is defined as the centre-to-centre distance to the width of foundations (S/B). Overall, 24 models are analyzed; and the results are compared and discussed in detail. It can be concluded that the presence of adjacent foundation leads to the reduction in bearing capacity for round shape footings while it can increase the bearing capacity of rectangular footings in some specific distances.

Keywords: bearing capacity, finite element analysis, loose sand, settlement equations, shallow foundation

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Authors: Nathi Ram, Saurabh K. Yadav

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In turbomachinery, the turbulent flow occurs due to the use of high velocity of low kinematic viscosity lubricants and used in many industrial applications. In the present work, the performance of symmetric slot-entry hybrid worn journal bearing under laminar and turbulent lubrication has been investigated. For turbulent lubrication, the Reynolds equation has been modified using Constantinescu turbulent model. This modified equation has been solved using the finite element method. The effect of turbulent lubrication on bearing’s performance has been presented for symmetric hybrid journal bearing. The slot-entry hybrid worn journal bearing under turbulent/laminar regimes have been investigated. It has been observed that the stiffness and damping coefficients are more for the bearing having slot width ratio (SWR) of 0.25 than the bearing with SWR of 0.5 and 0.75 under the turbulent regime. Further, it is also observed that for constant wear depth parameter, stability threshold speed gets increased for bearing operates at slot width ratio 0.25 under turbulent lubrication.

Keywords: hydrostatic bearings, journal bearings, restrictors, turbulent flow models, finite element technique

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Attila Kiss, Erzsébet Némedi, Zoltán Naár

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As natural prebiotic (non-digestible) carbohydrates stimulate the growth of colon microflora and contribute to maintain the health of the host, analytical studies aiming at revealing the chemical behavior of these beneficial food components came to the forefront of interest. Food processing (especially baking) may lead to a significant conversion of the parent compounds, hence it is of utmost importance to characterize the transformation patterns and the plausible decomposition products formed by thermal degradation. The relevance of this work is confirmed by the wide-spread use of these carbohydrates (fructo-oligosaccharides, cyclodextrins, raffinose and resistant starch) in the food industry. More and more functional foodstuffs are being developed based on prebiotics as bioactive components. 12 different types of oligosaccharides have been investigated in order to reveal their thermal degradation characteristics. Different carbohydrate derivatives (D-fructose and D-glucose oligomers and polymers) have been exposed to elevated temperatures (150 °C 170 °C, 190 °C, 210 °C, and 220 °C) for 10 min. An advanced HPLC method was developed and used to identify the decomposition products of carbohydrates formed as a consequence of thermal treatment. Gradient elution was applied with binary solvent elution (acetonitrile, water) through amine based carbohydrate column. Evaporative light scattering (ELS) proved to be suitable for the reliable detection of the UV/VIS inactive carbohydrate degradation products. These experimental conditions and applied advanced techniques made it possible to survey all the formed intermediers. Change in oligomer distribution was established in cases of all studied prebiotics throughout the thermal treatments. The obtained results indicate increased extent of chain degradation of the carbohydrate moiety at elevated temperatures. Prevalence of oligomers with shorter chain length and even the formation of monomer sugars (D-glucose and D-fructose) might be observed at higher temperatures. Unique oligomer distributions, which have not been described previously are revealed in the case of each studied, specific carbohydrate, which might result in various prebiotic activities. Resistant starches exhibited high stability when being thermal treated. The degradation process has been modeled by a plausible reaction mechanism, in which proton catalyzed degradation and chain cleavage take place.

Keywords: prebiotics, thermal degradation, fructo-oligosaccharide, HPLC, ELS detection

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Authors: Mohamed Raouf Benmakrelouf, Joseph Rynkiewicz

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Targeted maximum likelihood estimation (TMLE) is well-established method for causal effect estimation with desirable statistical properties. TMLE is a doubly robust maximum likelihood based approach that includes a secondary targeting step that optimizes the target statistical parameter. A causal interpretation of the statistical parameter requires assumptions of the Rubin causal framework. The causal effect of binary variable, E, on outcomes, Y, is defined in terms of comparisons between two potential outcomes as E[YE=1 − YE=0]. Our aim in this paper is to present an adaptation of TMLE methodology to estimate the causal effect of a non-binary categorical variable, providing a large application. We propose coding on the initial data in order to operate a binarization of the interest variable. For each category, we get a transformation of the non-binary interest variable into a binary variable, taking value 1 to indicate the presence of category (or group of categories) for an individual, 0 otherwise. Such a dummy variable makes it possible to have a pair of potential outcomes and oppose a category (or a group of categories) to another category (or a group of categories). Let E be a non-binary interest variable. We propose a complete disjunctive coding of our variable E. We transform the initial variable to obtain a set of binary vectors (dummy variables), E = (Ee : e ∈ {1, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when its category is not present, and the value of 1 when its category is present, which allows to compute a pairwise-TMLE comparing difference in the outcome between one category and all remaining categories. In order to illustrate the application of our strategy, first, we present the implementation of TMLE to estimate the causal effect of non-binary variable on outcome using simulated data. Secondly, we apply our TMLE adaptation to survey data from the French Political Barometer (CEVIPOF), to estimate the causal effect of education level (A five-level variable) on a potential vote in favor of the French extreme right candidate Jean-Marie Le Pen. Counterfactual reasoning requires us to consider some causal questions (additional causal assumptions). Leading to different coding of E, as a set of binary vectors, E = (Ee : e ∈ {2, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when the first category (reference category) is present, and the value of 1 when its category is present, which allows to apply a pairwise-TMLE comparing difference in the outcome between the first level (fixed) and each remaining level. We confirmed that the increase in the level of education decreases the voting rate for the extreme right party.

Keywords: statistical inference, causal inference, super learning, targeted maximum likelihood estimation

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Authors: Antonio Piga

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This study explores the correlation between Systemic Functional Linguistics (SFL) and Critical Discourse Analysis (CDA) as tools for analysing the evolution of rhetoric in the communicative strategies adopted in a company’s Reports on social and environmental responsibility. In more specific terms, transitivity and ergativity- concepts from Systemic Functional Linguistics (SFL) - through the lenses of CDA, are employed as a theoretical means for the analysis of a longitudinal study in the communicative strategies employed by Saras SpA pre- and during the Covid-19 pandemic crisis. Saras is an Italian joint-stock company operating in oil refining and power generation. The qualitative and quantitative linguistic analysis carried out through the use of Sketch Engine software aims to identify and explain how rhetoric - and ideology - is constructed and presented through language use in Saras SpA Sustainability Reports. Specific focus is given to communication strategies to local and global communities and stakeholders in the years immediately before and during the Covid-19 pandemic. The rationale behind the study lies in the fact that 2020 and 2021 have been among the most difficult years since the end of World War II. Lives were abruptly turned upside down by the pandemic, which had grave negative effects on people’s health and on the economy. The result has been a threefold crisis involving health, the economy and social tension, with the refining sector being one of the hardest hit, since the oil refining industry was one of the most affected industries due to the general reduction in mobility and oil consumption brought about by the virus-fighting measures. Emphasis is placed on the construction of rhetorical strategies pre- and during the pandemic crisis using the representational process of transitivity and ergativity (SFL), thus revealing the close relationship between the use language in terms of Social Actors and semantic roles of syntactic transformation on the one hand, and ideological assumptions on the other. The results show that linguistic decisions regarding transitivity and ergativity choices play a crucial role in how effective writing achieves its rhetorical objectives in terms of spreading and maintaining dominant and implicit ideologies and underlying persuasive actions, and that some ideological motivation is perpetuated – if not actually overtly or subtly strengthened - in social-environmental Reports issued in the midst of the Covid-19 pandemic crisis.

Keywords: systemic functional linguistics, sustainability, critical discourse analysis, transitivity, ergativity

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Aleksandar Hatzivelkos

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The term ”compromise” is mostly used casually within the social choice theory. It is usually used as a mere result of the social choice function, and this omits its deeper meaning and ramifications. This paper is based on a mathematical model for the description of a compromise as a version of the Sorites paradox. It introduces a formal definition of d-measure of divergence from a compromise and models a notion of compromise that is often used only colloquially. Such a model for vagueness phenomenon, which lies at the core of the notion of compromise enables the introduction of new mathematical structures. In order to maximize compromise, different methods can be used. In this paper, we explore properties of a social welfare function TdM (from Total d-Measure), which is defined as a function which minimizes the total sum of d-measures of divergence over all possible linear orderings. We prove that TdM satisfy strict Pareto principle and behaves well asymptotically. Furthermore, we show that for certain domain restrictions, TdM satisfy positive responsiveness and IIIA (intense independence of irrelevant alternatives) thus being equivalent to Borda count on such domain restriction. This result gives new opportunities in social choice, especially when there is an emphasis on compromise in the decision-making process.

Keywords: borda count, compromise, measure of divergence, minimization

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Authors: Mohammad Reza Dahmardeh Ghaleno, Mohammad Nohtani, Saeedeh Khaledi

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Hydrological models are applied to simulation and prediction floods in watersheds. WetSpa is a distributed, continuous and physically model with daily or hourly time step that explains of precipitation, runoff and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave Equation which depend on the slope, velocity and flow route characteristics. Garmabrood watershed located in Mazandaran province in Iran and passing over coordinates 53° 10´ 55" to 53° 38´ 20" E and 36° 06´ 45" to 36° 25´ 30"N. The area of the catchment is about 1133 km2 and elevations in the catchment range from 213 to 3136 m at the outlet, with average slope of 25.77 %. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe Model Efficiency Coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 61% and 83.17 % respectively.

Keywords: watershed simulation, WetSpa, runoff, flood prediction

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Authors: Ahmed Mateen, Zhu Qingsheng, Faisal Shahzad

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Requirement Engineering (RE) is a part being created for programming structure during the software development lifecycle. Software product line development is a new topic area within the domain of software engineering. It also plays important role in decision making and it is ultimately helpful in rising business environment for productive programming headway. Decisions are central to engineering processes and they hold them together. It is argued that better decisions will lead to better engineering. To achieve better decisions requires that they are understood in detail. In order to address the issues, companies are moving towards Software Product Line Engineering (SPLE) which helps in providing large varieties of products with minimum development effort and cost. This paper proposed a new framework for software product line and compared with other models. The results can help to understand the needs in SPL testing, by identifying points that still require additional investigation. In our future scenario, we will combine this model in a controlled environment with industrial SPL projects which will be the new horizon for SPL process management testing strategies.

Keywords: requirements engineering, software product lines, scoping, process structure, domain specific language

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Authors: Babak Kamrani, Nozar Kishi

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Stochastic catalog represents the events module of the earthquake loss estimation models. It includes series of events with different magnitudes and corresponding frequencies/probabilities. For the development of the stochastic catalog, random or uniform sampling methods are used to sample the events from the seismicity model. For covering all the Magnitude Frequency Distribution (MFD), a huge number of events should be generated for the above-mentioned methods. Characteristic Event (CE) method chooses the events based on the interest of the insurance industry. We divide the MFD of each source into bins. We have chosen the bins based on the probability of the interest by the insurance industry. First, we have collected the information for the available seismic sources. Sources are divided into Fault sources, subduction, and events without specific fault source. We have developed the MFD for each of the individual and areal source based on the seismicity of the sources. Afterward, we have calculated the CE magnitudes based on the desired probability. To develop the stochastic catalog, we have introduced uncertainty to the location of the events too.

Keywords: stochastic catalogue, earthquake loss, uncertainty, characteristic event

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Authors: Mustafa Kaya

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In this study, the effect of pre-stressed strand diameters, providing the beam-to-column connections, was investigated from both experimental, and analytical aspects. In the experimental studies, the strength, stiffness, and energy dissipation capacities of the precast specimens comprising two pre-stressed strand samples of 12.70 mm, and 15.24 mm diameters, were compared with the reference specimen. The precast specimen with strands of 15.24 mm reached 96% of the maximum strength of the reference specimen; the amount of energy dissipated by this specimen until end of the test reached 48% of the amount of energy dissipated by the reference sample, and the stiffness of the same specimen at a 1.5% drift of reached 77% of the stiffness of the reference specimen at this drift. Parallel results were obtained during the analytical studies from the aspects of strength, and behavior, but the initial stiffness of the analytical models was lower than that of the test specimen.

Keywords: precast beam to column connection, moment resisting connection, post tensioned connections, finite element method

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Authors: N. Pérez-Rodríguez, D. García-Bernet, A. Torrado-Agrasar, J. M. Cruz, A. B. Moldes, J. M. Domínguez

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World economy is based on non-renewable, fossil fuels such as petroleum and natural gas, which entails its rapid depletion and environmental problems. In EU countries, the objective is that at least 20% of the total energy supplies in 2020 should be derived from renewable resources. Biogas, a product of anaerobic degradation of organic substrates, represents an attractive green alternative for meeting partial energy needs. Nowadays, trend to circular economy model involves efficiently use of residues by its transformation from waste to a new resource. In this sense, characteristics of agricultural residues (that are available in plenty, renewable, as well as eco-friendly) propitiate their valorisation as substrates for biogas production. Corn cob is a by-product obtained from maize processing representing 18 % of total maize mass. Corn cob importance lies in the high production of this cereal (more than 1 x 109 tons in 2014). Due to its lignocellulosic nature, corn cob contains three main polymers: cellulose, hemicellulose and lignin. Crystalline, highly ordered structures of cellulose and lignin hinders microbial attack and subsequent biogas production. For the optimal lignocellulose utilization and to enhance gas production in anaerobic digestion, materials are usually submitted to different pretreatment technologies. In the present work, enzymatic hydrolysis, ultrasounds and combination of both technologies were assayed as pretreatments of corn cob for biogas production. Enzymatic hydrolysis pretreatment was started by adding 0.044 U of Ultraflo® L feruloyl esterase per gram of dry corncob. Hydrolyses were carried out in 50 mM sodium-phosphate buffer pH 6.0 with a solid:liquid proportion of 1:10 (w/v), at 150 rpm, 40 ºC and darkness for 3 hours. Ultrasounds pretreatment was performed subjecting corn cob, in 50 mM sodium-phosphate buffer pH 6.0 with a solid: liquid proportion of 1:10 (w/v), at a power of 750W for 1 minute. In order to observe the effect of the combination of both pretreatments, some samples were initially sonicated and then they were enzymatically hydrolysed. In terms of methane production, anaerobic digestion of the corn cob pretreated by enzymatic hydrolysis was positive achieving 290 L CH4 kg MV-1 (compared with 267 L CH4 kg MV-1 obtained with untreated corn cob). Although the use of ultrasound as the only pretreatment resulted detrimentally (since gas production decreased to 244 L CH4 kg MV-1 after 44 days of anaerobic digestion), its combination with enzymatic hydrolysis was beneficial, reaching the highest value (300.9 L CH4 kg MV-1). Consequently, the combination of both pretreatments improved biogas production from corn cob.

Keywords: biogas, corn cob, enzymatic hydrolysis, ultrasound

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Authors: Mohanad Al-Behadili, Djamila Ouelhadj

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In this paper, a specific type of vehicle routing problem under stochastic demand (SVRP) is considered. This problem is of great importance because it models for many of the real world vehicle routing applications. This paper used a robust optimisation model to solve the problem along with the novel Simulation-Particle Swarm Optimisation (Sim-PSO) approach. The proposed Sim-PSO approach is based on the hybridization of the Monte Carlo simulation technique with the PSO algorithm. A comparative study between the proposed model and the Sim-PSO approach against other solution methods in the literature has been given in this paper. This comparison including the Analysis of Variance (ANOVA) to show the ability of the model and solution method in solving the complicated SVRP. The experimental results show that the proposed model and Sim-PSO approach has a significant impact on the obtained solution by providing better quality solutions comparing with well-known algorithms in the literature.

Keywords: stochastic vehicle routing problem, robust optimisation model, Monte Carlo simulation, particle swarm optimisation

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Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

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De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

Procedia PDF Downloads 78

Authors: Jasminka Mujic, Karin Milde-Langosch, Volkmar Mueller, Mirza Suljagic, Tea Becirevic, Jozo Coric, Daria Ler

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MACC1 (metastasis associated in colon cancer-1) is a prognostic biomarker for tumor progression, metastasis, and survival of a variety of solid cancers. MACC1 also causes tumor growth in xenograft models and acts as a master regulator of the HGF/MET signaling pathway. In breast cancer, the expression of MACC1 determined by immunohistochemistry was significantly associated with positive lymph node status and advanced clinical stage. The aim of the present study was to further investigate the prognostic or predictive value of MACC1 expression in breast cancer using western blot analysis and immunohistochemistry. The results of our study have shown that high MACC1 expression in breast cancer is associated with shorter disease-free survival, especially in node-negative tumors. The MACC1 might be a suitable biomarker to select patients with a higher probability of recurrence which might benefit from adjuvant chemotherapy. Our results support a biologic role and potentially open the perspective for the use of MACC1 as predictive biomarker for treatment decision in breast cancer patients.

Keywords: breast cancer, biomarker, HGF/MET, MACC1

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Authors: Nikola Vlahovic, Blazenka Knezevic, Petra Batalic

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In this paper, we will present a research about feasibility of implementing unmanned aerial vehicles, also known as 'drones', in logistics. Research is based on available information about current incentives and experiments in application of delivery drones in commercial use. Overview of current pilot projects and literature, as well as an overview of detected challenges, will be compiled and presented. Based on these findings, we will present a conceptual model of business process that implements delivery drones in business to business logistic operations. Business scenario is based on a pharmaceutical supply chain. Simulation modeling will be used to create models for running experiments and collecting performance data. Comparative study of the presented conceptual model will be given. The work will outline the main advantages and disadvantages of implementing unmanned aerial vehicles in delivery services as a supplementary distribution channel along the supply chain.

Keywords: business process, delivery drones, logistics, simulation modelling, unmanned aerial vehicles

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Authors: Sandesh Achar

Abstract:

Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

Procedia PDF Downloads 77

Authors: Visvaganthie Moodley

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Language use and aesthetic appreciation are integral to meaning-making in prose, as they are in poetry. However, in comparison to poetic analysis, a literary analysis of prose that focuses on linguistics and stylistics is somewhat scarce as it generally requires the study of lengthy texts. Nevertheless, the effect of linguistic and stylistic features in prose as conscious design by authors for creating specific effects and conveying preconceived messages is drawing increasing attention of linguists and literary experts. A close examination of language use in prose can, among a host of literary purposes, convey emotive and cognitive values and contribute to making interpretations about how fictional characters are represented to the imaginative reader. This paper provides a literary analysis of Yann Martel’s narrative of a 14-year-old Indian boy, Pi, who had survived the wreck of a Japanese cargo ship, by focusing on his 227-day journey of tribulations, along with a Bengal tiger, on a lifeboat. The study favours a pluralistic approach blending literary criticism, linguistic analysis and stylistic description. It adopts Leech and Short’s (2007) broad framework of linguistic and stylistic categories (lexical categories, grammatical categories, figures of speech etc. [sic] and context and cohesion) as well as a range of other relevant linguistic phenomena to show how the narrator, Pi, and the author influence the reader’s interpretations of Pi’s character. Such interpretations are made using the lens of Freud’s psychoanalytical theory (which focuses on the interplay of the instinctual id, the ego and the moralistic superego) and Blake’s philosophy of innocence and experience (the two contrary states of the human soul). The paper traces Pi’s transformation from animal-loving, God-fearing vegetarian to brutal animal slayer and cannibal in his journey of survival. By a close examination of the linguistic and stylistic features of the narrative, it argues that, despite evidence of butchery and cannibalism, Pi’s gruesome behaviour is motivated by extreme physiological and psychological duress and not intentional malice. Finally, the paper concludes that the voice of the narrator, Pi, and that of the author, Martel, act as powerful persuasive agents in influencing the reader to respond with a sincere flow of sympathy for Pi and judge him as having retained his innocence in his instinctual need for survival.

Keywords: foregrounding, innocence and experience, lexis, literary analysis, psychoanalytical lens, style

Procedia PDF Downloads 137

Authors: H. Shokouhmand, H. Sobhani, B. Sajadi, M. Degheh

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In recent years, experimental and numerical investigation of water UV reactors has increased significantly. The main drawback of experimental methods is confined and expensive survey of UV reactors features. In this study, a CFD model utilizing the eulerian-lagrangian framework is applied to analysis the disinfection performance of a closed conduit reactor which contains four UV lamps perpendicular to the flow. A discrete ordinates (DO) model was employed to evaluate the UV irradiance field. To investigate the importance of each of lamps on the inactivation performance, in addition to the reference model (with 4 bright lamps), several models with one or two bright lamps in various arrangements were considered. All results were reported in three inactivation kinetics. The results showed that the log inactivation of the two central bright lamps model was between 88-99 percent, close to the reference model results. Also, whatever the lamps are closer to the main flow region, they have more effect on microbial inactivation. The effect of some operational parameters such as water flow rate, inlet water temperature, and lamps power were also studied.

Keywords: Eulerian-Lagrangian framework, inactivation kinetics, log inactivation, water UV reactor

Procedia PDF Downloads 225

Authors: Tarek M. Alguhane, Ayman H. Khalil, Nour M. Fayed, Ayman M. Ismail

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An existing RC building in Madinah is seismically evaluated with and without infill wall. Four model systems have been considered i. e. model I (no infill), model IIA (strut infill-update from field test), model IIB (strut infill- ASCE/SEI 41) and model IIC (strut infill-Soft storey-ASCE/SEI 41). Three dimensional pushover analyses have been carried out using SAP 2000 software incorporating inelastic material behavior for concrete, steel and infill walls. Infill wall has been modeled as equivalent strut according to suggested equation matching field test measurements and to the ASCE/SEI 41 equation. The effect of building modeling on the performance point as well as capacity and demand spectra due to EQ design spectrum function in Madinah area has been investigated. The response modification factor (R) for the 5 story RC building is evaluated from capacity and demand spectra (ATC-40) for the studied models. The results are summarized and discussed.

Keywords: infill wall, pushover analysis, response modification factor, seismic assessment

Procedia PDF Downloads 365