Title: Quantitative analysis of biokinetics and internal dosimetry in a dynamic 3D-printed thyroid phantom

Abstract:This study aims to simulate the recent thyroidal kinetics data of 99mTc­pertechnetate from literature in a 3D-printed anatomically realistic thyroid phantom and to compare the resulting radiation dose with the ICRP publication and OLINDA/EXM software. \nA dynamic technique was applied by using an infusion pump via one infusion and drain pole connected the thyroid phantom to an auxiliary silicon-based hose. This is to simulate the biokinetics of influx and efflux of the 99mTc inside the thyroid gland. \nThe corrected accumulated activities of 99mTc-pertechnetate inside the thyroid from ICRP and the recent publication were applied, where two concentration 4 % and 7 % of the administered activity (370MBq) were used.\nThe radiation dose was measured inside each lobe of the 3D printed thyroid phantom using two sets of TLD and OSL dosimeters. The results compared with the calculated values from ICRP and the OLINDA/EXM internal dosimetry computer program in both concentrations.\nThe results showed slightly similar values from ICRP and the dosimeters results for the kinetics data of 7% concentration. Meanwhile, it showed a noticeable difference when the 4% kinetics data applied with a percentage difference of 28%. However, the results from OLINDA/EXM showed a significant difference when comparing with the ICRP and the mean dose of the dosimeters from both kinetics data applied. \nThe different values obtained from the practical measurements could refer to the biokinetics technique used and to the precise geometry size and shape of the 3D-printed thyroid phantom as well as the positions of the dosimeters inside the phantom. Therefore, this technique can be used to evaluate the absorbed dose as an alternative and practical method for an individualised dosimetric approach. This study suggests that the ICRP could take into account and include the most recent published studies, where it still refers to the publication and models published more than 40 years ago

Title: A convexity in fuzzy metric space and nonexpansive mappings

Abstract:In this paper,we introduce the notion of convexity in fuzzy metric spaces and we study the structures of fuzzy metric spaces. Also, we present some theorems on the existence of coincidence points in fuzzy convex metric spaces. Next, we will define the concept of a star-shaped subsets in fuzzy convex metric space. We were able to prove some fixed point theorems for commuting mappings of the non-expansive type mappings on a star-shaped subset of fuzzy convex metric spaces. Finally, we also generalize the notion of fuzzy convex metric space, give a non-trivial example of such a space. Also, we obtained some fixed point theorems for multi-valued mappings of non-expansive type.

Title: Temporal analysis and forecast of surface air temperature using SARIMA and SVAR models: case study in Bogota, Colombia

Abstract:In this work, we study the short�term dynamics of the Surface Air Temperature (SAT) using data obtained from a meteorological station in Bogot� from 2009 to 2019 and using time series. The data that we used correspond to the monthly mean of the historical registers of SAT and three pollutants. A descriptive analysis of the data follows. Then, some predictions are obtained from two different approaches: (i) a univariate analysis of SAT through a SARIMA model, which shows a good fit; and (ii) a multivariate analysis of SAT and pollutants using a SVAR model. Suitable transformations were first applied on the original dataset to work with stationary time series. Subsequently, A SARIMA model and a VAR(2) with its associated SVAR model are estimated. Furthermore, we obtain one�year forecasts for the logarithm of SAT in both models. Our forecasts simulate the natural fluctuation of SAT, presenting peaks and valleys in months when SAT is high and low, respectively. The SVAR model allows us to identify certain shocks that affect the instant relationships among variables. These relations were studied by the impulse�response function and the VAR model variance decomposition. The results are consistent with environmental theories.


Abstract:The contribution summarizes author’s experiences of leading a course on process modelling & simulation at Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic. It presents contents of the course for both, lectures and tutorials together with the used methods and software tools. Necessary requirements for passing the course successfully are also provided together with some statistics concerning students’ results. The paper ends presenting one typical students’ final project followed by teacher’s reflection on this course and its possible future directions. As such, this paper can serve as an inspiration for other similarly oriented departments where modelling & simulation tools and methods play important roles in engineering education.

Title: Using the Hybrid MCDM Model to Improve the Quality of Airports� Informative Service Setting Items

Abstract:As air travelling increases rapidly past decades, airport service providers need to improve the quality of their airports’ informative service setting items (ISSI) in order to enhance value of the service and to satisfy travellers. This study therefore suggests several ways to improve the service quality using a multiple criteria decision-making (MCDM) model by combining a decision-making trial and evaluation laboratory (DEMATEL) method with a Višekriterijumsko Kompromisno Rangiranje (VIKOR) method to assess service performance. The assessment result provides an influential network relationship map (INRM), finding the influential factors with a DEMATEL-based analytic network process (DANP). The result shows that the quality of information and service staffs’ attitudes toward ISSI are important factors to consider when improving service quality. The quality of information also influences service staffs’ attitudes as well as visitors’ knowledge and involvement in the service. The knowledge of the systems and services and users’ participation are also important, whereas the reliability of ISSI and the relevance of information service are less important. In general, we find that there is still a need for improving the serving quality of ISSI at airports. This result provides managers of airports and airlines with a knowledge-based understanding of strategies to satisfy air travellers’ needs and to encourage them to use airports’ ISSI more actively.

Title: Weibull-Linear Exponential Distribution

Abstract:In this article, a new four-parameter lifetime distribution, namely, Weibull-Linear ex- ponential distribution is defined and studied. A several of its structural properties such as quartiles, moments, mean waiting time, mean residual lifetime, Renyi entropy, mode and order statistics are derived. Based on the idea of the Weibull T − X family which was proposed by Alzaatreh [6] the new density function of this model is developed. The model parameters, as well as some of the lifetime parameters (reliability and failure rate functions), are estimated using the maximum likelihood method. Asymptotic confidence intervals estimates of the model parameters are also evaluated by using the Fisher informa- tion matrix. Moreover, to construct the asymptotic confidence intervals of the reliability and failure rate functions, we need to find the variance of them, which are approximated by the delta method. A real data set is used to illustrate the application of the Weibull-Linear Exponential distribution. The new distribution can be considered an alternative model to other lifetime distributions which can be fit for modeling positive real data in many fields.

Title: The child’s emotional speech classification by human: Indian-Russian cross-cultural pilot study

Abstract:The goal of the study is to compare the recognition of four emotions “joy-neutral-sadness-anger” in different types of child’s speech across two languages: Russian and Tamil. The participants of the study were 8-12 year-old children: 12 Russian speaking children (born and living in St. Petersburg, Russia) and 18 Tamil speaking children (born and living in Vellore, India); 26 adults – listeners by 13 native speakers of the Russian and Tamil languages. The speech materials were spontaneous speech and “acting” speech. It is shown that Russian and Indian experts are capable to recognize correctly the emotional states of children by their speech, but with varying accuracy. The native Russian and Tamil speaking experts were more accurate in recognizing the emotional states of children in their native language, in the “acting” speech vs. spontaneous speech. The data of the cross-cultural study support the view that emotional speech includes universal and culture–specific features.

Title: LDPC-Coded Turbo Equalization for Underwater Wireless Optical Communication

Abstract:In underwater wireless optical communication (UWOC) systems, scattering and absorption occur due to water molecules and suspended particles, resulting in weak signals at the receiver end. In this study, we employed a low-density parity-check (LDPC) code, which is a kind of error-correcting code, in order to compensate for performance loss, and its performance was improved only when the input values of the decoder were soft decision types. However, no algorithm has yet been reported that applies a soft decision technique for the M-ary pulse position modulation (PPM) and quadrature amplitude modulation (QAM) schemes in the case of UWOC. Therefore, we developed a soft value generator (SVG) algorithm in order to use a turbo equalizer, which improves the performance over all of the iterations in the case of the M-ary PPM and QAM schemes. Through simulations, we confirmed that the proposed method performs better than the conventional hard decision algorithm. We also evaluated the performance of the proposed method through water tank experiments, in which M-ary PPM and QAM data were employed to perform experiments by varying the turbidity and transmission rates in a water tank. This again showed that the performance of the proposed algorithm is superior to that of the conventional algorithm.

Title: Impact of Health Literacy Intervention on Self-Efficacy and Prenatal Care

Abstract:Health literacy is applied on social cognitive skills that determine motivation and capability of individuals in achievement, perception, and using information in such a way that leads in preservation and promotion of their health. Current research was conducted aiming at determining impact of health literacy intervention in pregnant women on self-efficacy and prenatal care. \nMethods:It is an experimental study carried out on 90 pregnant women (45 per groups) living in Iranshahr. Multistage random sampling method was used. Educational intervention based on health literacy and empowerment of pregnant women was carried out within one month in group and individual manner in case group. Data collection tool included maternal health literacy survey (MHLAP) and self-efficacy of pregnant women survey. Data were analyzed using SPSS version 18 software and independent t-test, pairwise t test, and chi-square test. The significance level was considered <0.5 .\nResults: Unlike control group, significant difference was observed in average health literacy, self-efficacy, and prenatal care behaviors after educational intervention in intervention group P˂0.001). Average health literacy, self-efficacy, and prenatal care behaviors increased to 21.62, 22.21, and 9.13 percent, respectively, in case group compared to before intervention. \nConclusion: Strategies of health literacy promotion should be developed in order to promote health literacy and thus self-efficacy and prenatal care.

Title: A Voting Ensemble method for Hate Speech Detection in social media

Abstract:Dissemination of hate speech in social media networking platforms such as twitter has been on a steady increase as internet usage increases throughout the world. There is a plethora of research works on machine learning algorithms for automated hate speech detection. Natural language presents many ambiguities, which may not be logical for machines to understand. For instance, the context of the discussion determines the semantics of its interpretation. Consequently, there has been a lot of work on this problem. In recent years, deep learning has shown some promising results but require vast amounts of data for training. The major limitation of classical algorithms, on the other hand, emanates from high variance. This challenge can be addressed by harnessing the strengths of different methods using an ensemble. In this paper, we present a Voting ensemble method that takes the advantages of the Logistic Regression (LR), Support Vector Machine (SVM) and Decision Trees (DT) as base classifiers for the task of hate speech detection. The aim of this research paper is to show the superior performance of the voting ensemble as compared to ten state-of-the-art machine learning algorithms. Experimental results show that the voting ensemble outperformed both the deep learning and classical algorithms using eight popular performance evaluation metrics.