Search results for: Full Bayes models

Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Petra Buzkova, Milos Kopa

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Reliability of the credit default swap market had been questioned repeatedly during the EMU debt crisis. The article examines whether this development influenced sovereign EMU CDS prices in general. We regress the CDS market price on a model risk neutral CDS price obtained from an adopted reduced form valuation model in the 2009-2013 period. We look for a break point in the single-equation and multi-equation econometric models in order to show the changes in relations between CDS market and model prices. Our results differ according to the risk profile of a country. We find that in the case of riskier countries, the relationship between the market and model price changed when market participants started to question the ability of CDS contracts to protect their buyers. Specifically, it weakened after the change. In the case of less risky countries, the change happened earlier and the effect of a weakened relationship is not observed.

Keywords: chow stability test, credit default swap, debt crisis, reduced form valuation model, seemingly unrelated regression

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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: G. St-Pierre-Lemieux, E. Askari Mahvelati, D. Groleau, P. Proulx

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Mathematical modeling has become an important tool for the study of foam behavior. Computational Fluid Dynamic (CFD) can be used to investigate the behavior of foam around foam breakers to better understand the mechanisms leading to the ‘destruction’ of foam. The focus of this investigation was the simple cone foam breaker, whose performance has been identified in numerous studies. While the optimal pumping angle is known from the literature, the contribution of pressure drop, shearing, and centrifugal forces to the foam syneresis are subject to speculation. This work provides a screening of those factors against changes in the cone angle and foam rheology. The CFD simulation was made with the open source OpenFOAM toolkits on a full three-dimensional model discretized using hexahedral cells. The geometry was generated using a python script then meshed with blockMesh. The OpenFOAM Volume Of Fluid (VOF) method was used (interFOAM) to obtain a detailed description of the interfacial forces, and the model k-omega SST was used to calculate the turbulence fields. The cone configuration allows the use of a rotating wall boundary condition. In each case, a pair of immiscible fluids, foam/air or water/air was used. The foam was modeled as a shear thinning (Herschel-Buckley) fluid. The results were compared to our measurements and to results found in the literature, first by computing the pumping rate of the cone, and second by the liquid break-up at the exit of the cone. A 3D printed version of the cones submerged in foam (shaving cream or soap solution) and water, at speeds varying between 400 RPM and 1500 RPM, was also used to validate the modeling results by calculating the torque exerted on the shaft. While most of the literature is focusing on cone behavior using Newtonian fluids, this works explore its behavior in shear thinning fluid which better reflects foam apparent rheology. Those simulations bring new light on the cone behavior within the foam and allow the computation of shearing, pressure, and velocity of the fluid, enabling to better evaluate the efficiency of the cones as foam breakers. This study contributes to clarify the mechanisms behind foam breaker performances, at least in part, using modern CFD techniques.

Keywords: bioreactor, CFD, foam breaker, foam mitigation, OpenFOAM

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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: Nasser Almonawer

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Timely reaction to an evolving global business environment and volatile market conditions necessitates system and process flexibility, which in turn demands agile and adaptable architecture and a steady infusion of affordable new technologies. On the contrary, a large number of organizations utilize systems characterized by inflexible and obsolete legacy architectures. To effectively respond to the dynamic contemporary business environments, such architectures must be migrated to robust and modular open architectures. To this end, this paper proposes an integrated decision support system for a seamless migration to open systems. The proposed decision support system (DSS) integrates three well-established quantitative and qualitative decision-making models—namely, the Delphi method, Analytic Hierarchy Process (AHP) and Goal Programming (GP) to (1) assess risks and establish evaluation criteria; (2) formulate migration strategy and rank candidate systems; and (3) allocate resources among the selected systems.

Keywords: decision support systems, open systems architecture, analytic hierarchy process (AHP), goal programming (GP), delphi method

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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: E. Syngelaki, C. C. F. Schinkel, S. Klatt, E. Hörandl

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Environmental influence can alter the conditions for plant development and can trigger changes in epigenetic variation. Thus, the exposure to abiotic environmental stress can lead to different DNA methylation profiles and may have evolutionary consequences for adaptation. Epigenetic control mechanisms may further influence mode of reproduction. The alpine species R. kuepferi has diploid and tetraploid cytotypes, that are mostly sexual and facultative apomicts, respectively. Hence, it is a suitable model system for studying the correlations of mode of reproduction, ploidy, and environmental stress. Diploid and tetraploid individuals were placed in two climate chambers and treated with low (+7°C day/+2°C night, -1°C cold shocks for three nights per week) and warm (control) temperatures (+15°C day/+10°C night). Subsequently, methylation sensitive-Amplified Fragment-Length Polymorphism (AFPL) markers were used to screen genome-wide methylation alterations triggered by stress treatments. The dataset was analyzed for four groups regarding treatment (cold/warm) and ploidy level (diploid/tetraploid), and also separately for full methylated, hemi-methylated and unmethylated sites. Patterns of epigenetic variation suggested that diploids differed significantly in their profiles from tetraploids independent from treatment, while treatments did not differ significantly within cytotypes. Furthermore, diploids are more differentiated than the tetraploids in overall methylation profiles of both treatments. This observation is in accordance with the increased frequency of apomictic seed formation in diploids and maintenance of facultative apomixis in tetraploids during the experiment. Global analysis of molecular variance showed higher epigenetic variation within groups than among them, while locus-by-locus analysis of molecular variance showed a high number (54.7%) of significantly differentiated un-methylated loci. To summarise, epigenetic variation seems to depend on ploidy level, and in diploids may be correlated to changes in mode of reproduction. However, further studies are needed to elucidate the mechanism and possible functional significance of these correlations.

Keywords: apomixis, cold stress, DNA methylation, Ranunculus kuepferi

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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: Hema Vanita Kesevan

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The global spread of Covid-19 virus in early 2020 has led to major changes in many walks of life, including the education system. Traditional face to face lessons that were carried out for years has been replaced by online learning. Although online learning has been used before the pandemic, it has not been the only source of teaching and learning. This drastic change has brought significant impact to the process of teaching and learning in many classrooms around the world. Likewise, in country like Malaysia that that has been promoting online learning but has not utilize it fully due to many restrictions in terms of technology, accessibility, and online literacy, the sudden change to full online platform learning in all educational sector has definitely caused Issues in terms of its adaptation and usage. Although many studies have been conducted to explore the efficiency and impact of online learning during the pandemic, studies focusing on the same are limited in Malaysian classroom context, especially in English language classrooms. Thus, this study seeks to explore on the efficacy and effectiveness of online learning tools in ESL classroom contexts during the pandemic. The aim of this study is to understand the educator's and student's perceptions on the implementation of online learning tools in the teaching and learning process and the types of online learning tools that were used to assist the teaching and learning process during the pandemic. Particularly, this study focused to explore the types of online learning tools used in Malaysian schools and university during the online teaching and learning process and further explores how the various types of tools used impacted the students' participation in the lessons conducted. The participants of this study are secondary school students, teachers, and university students. Data will be collected in terms of survey questionnaire and interviews. The survey data intends to obtain information on the types of online learning used in ESL teaching and learning practices during the pandemic, how the various types of online tools influence students' participation during lessons. The interview data from the teachers serves to provide information about the selection of online learning tools, challenges of using it to conduct online lessons, and other arising issues. A mixed method design will be used to analysed the data obtained. The questionnaire will be analysed quantitatively using descriptive analysis meanwhile, the interview data will be analysed qualitatively.

Keywords: Covid 19, online learning tools, ESL classroom, effectiveness, efficacy

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Authors: Katherine van Kampen, Reva Qiu, Patricia Farrugia

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Orthopedic surgery has fallen behind when it comes to gender diversity despite medical school classes reaching gender parity. Studies have shown that orthopedic surgery would require 117 years to reach gender parity with the trainee population, the longest time than any other specialty, including neurosurgery, urology, and otolaryngology. The barriers that face women in orthopedic surgery have been well researched, with contributing factors being on-going stereotypes of the field, lack of women mentors, and gender roles outside of the hospital. Furthermore, women in orthopedic surgery face barriers to achieve promotion, publications, and leadership roles leading to a “leaky pipeline,” resulting in less and less women in key academic roles in the field. It is a complex topic with barriers and challenges faced in medical school, residency, and throughout employment. Our scoping review seeks to understand these challenges across a temporal timeline and to further characterize such barriers and the driving factors behind them. To this date, authors did not find a scoping review that seeks to look broadly at factors impacting the decreased amount of women entering orthopedics and the factors that cause women to hit a “glass ceiling”, the idea that women will not achieve the same success as men despite the same qualifications, upon entering the field. This scoping review is the first of its kind to attempt to summarize the large body of research focusing on women in orthopedic surgery from the preconceptions in medical school impacting their desire to pursue orthopedics all the way to employment, including challenges to academic success and financial success. Literature databases will be searched with the following key terms: women, gender inequity, workforce, orthopedics, and citations will be hand searched and collected. Articles included will discuss gender inequality within orthopedics with non-english, patient related articles excluded. Full-text review will seek to characterize the specific barriers faced by women across medical school, residency, and employment. Themes that are expected to be highlighted are workforce data, women in orthopedic leadership, medical student perspectives on the specialty, and gender bias and discrimination in the field.

Keywords: orthopedics, gender equity, workforce, women in surgery

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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

Abstract:

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: Nadwah Onwi, Puspa Maniam, Azmawanie A. Aziz, Fairus Mukhtar, Nor Azrita Mohamed Zin, Nurul Haslina Mohd Zin, Nurul Fatehah Ismail, Mohamad Safwan Yusoff, Susilidianamanalu Abd Rahman, Siti Munirah Harris, Maryam Aizuddin

Abstract:

Objective: Malaysia has a growing number of individuals with complex communication needs (CCN). The initiation of augmentative and alternative communication (AAC) intervention may facilitate individuals with CCN to understand and express themselves optimally and actively participate in activities in their daily life. AAC is defined as multimodal use of communication ability to allow individuals to use every mode possible to communicate with others using a set of symbols or systems that may include the symbols, aids, techniques, and strategies. It is consequently critical to evaluate the deficits to inform treatment for AAC intervention. However, no known measurement tools are available to evaluate the user with CCN available locally. Design: A systematic review (SR) is designed to analyze the psychometric properties of AAC assessment for adolescents with CCN published in peer-reviewed journals. Tools are rated by the methodological quality of studies and the psychometric measurement qualities of each tool. Method: A literature search identifying AAC assessment tools with psychometrically robust properties and conceptual framework was considered. Two independent reviewers screened the abstracts and full-text articles and review bibliographies for further references. Data were extracted using standardized forms and study risk of bias was assessed. Result: The review highlights the psychometric properties of AAC assessment tools that can be used by speech-language therapists applicable to be used in the Malaysian context. The work outlines how systematic review methods may be applied to the consideration of published material that provides valuable data to initiate the development of Malay Language AAC assessment tools. Conclusion: The synthesis of evidence has provided a framework for Malaysia Speech-Language therapists in making an informed decision for AAC intervention in our standard operating procedure in the Ministry of Health, Malaysia.

Keywords: augmentative and alternative communication, assessment, adolescents, complex communication needs

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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

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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 93