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<![CDATA[Music Stimulation as a Method of Optimizing Autobiographical Memory in Patients Diagnosed with Alzheimer’s Disease ]]>
<![CDATA[Ariana Ponce-Pardo]]>;
<![CDATA[Pamela Acosta-Rodas]]>;
<![CDATA[Jorge Cruz-Cárdenas]]>;
<![CDATA[Carlos Ramos-Galarza]]>
<![CDATA[ Emerging Science Journal Vol 5, No 5 (2021): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01304
<![CDATA[Alzheimer’s is a neurodegenerative disease characterized by the progressive deterioration of cognitive functions, with memory being the most affected. Several studies have shown the benefits of music as a complementary treatment for dementia, improving patients’ quality of life. A scientific contribution is needed to show how autobiographic memory could be improved by using musical activities. Objective: The aim of this investigation is to analyze the impact of a musical stimulation protocol on the performance of autobiographical memory in elderly people suffering from Alzheimer’s. Participants and Method: This research was conducted with three patients diagnosed with Alzheimer’s disease: two females (66.7%), and one male (33.3%). One (33.3%) was in the early stages, and two were in the middle stages. This investigation used a quantitative, pre-experimental, longitudinal study with the application of two tests before and after the intervention. Findings: Changes in the performance of autobiographical memory (t=-5.79, p=0.002), and in the semantic component (t=-10.14, p=0.01) were found to be statistically significant, but no changes were evident for episodic memory (t=-0.19, p=0.86). Conclusion: This study provides preliminary evidence of the potential effectiveness of using a music protocol to improve the performance of autobiographical memory in patients with Alzheimer's Disease. Doi: 10.28991/esj-2021-01304 Full Text: PDF]]>
<![CDATA[Design Low Order Robust Controller for the Generator’s Rotor Angle Stabilization PSS System ]]>
<![CDATA[Vu Ngoc Kien]]>;
<![CDATA[Nguyen Hien Trung]]>;
<![CDATA[Nguyen Hong Quang]]>
<![CDATA[ Emerging Science Journal Vol 5, No 5 (2021): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01299
<![CDATA[The electrical system's problem stabilizes the electrical system with three primary parameters: rotor angle stability, frequency stability, and voltage stability. This paper focuses on the problem of designing a low-order stable optimal controller for the generator rotor angle (load angle) stabilization system with minor disturbances. These minor disturbances are caused by lack of damping torque, change in load, or change in a generator during operation. Using the RH∞optimal robust design method for the Power System Stabilizer (PSS) to stabilize the generator’s load angle will help the PSS system work sustainably under disturbance. However, this technique's disadvantage is that the controller often has a high order, causing many difficulties in practical application. To overcome this disadvantage, we propose to reduce the order of the higher-order optimal robust controller. There are two solutions to reduce order for high-order optimal robust controller: optimal order reduction according to the given controller structure and order reduction according to model order reduction algorithms. This study selects the order reduction of the controller according to the model order reduction algorithms. In order to choose the most suitable low-order optimal robust controller that can replace the high-order optimal robust controller, we have compared and evaluated the order-reducing controllers according to many model order reduction algorithms. Using robust low-order controllers to control the generator’s rotor angle completely meets the stabilization requirements. The research results of the paper show the correctness of the controller order reduction solution according to the model order reduction algorithms and open the possibility of application in practice. Doi: 10.28991/esj-2021-01299 Full Text: PDF]]>
<![CDATA[Bayesian Approaches for Poisson Distribution Parameter Estimation ]]>
<![CDATA[Yadpirun Supharakonsakun]]>
<![CDATA[ Emerging Science Journal Vol 5, No 5 (2021): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01310
<![CDATA[The Bayesian approach, a non-classical estimation technique, is very widely used in statistical inference for real world situations. The parameter is considered to be a random variable, and knowledge of the prior distribution is used to update the parameter estimation. Herein, two Bayesian approaches for Poisson parameter estimation by deriving the posterior distribution under the squared error loss or quadratic loss functions are proposed. Their performances were compared with frequentist (maximum likelihood estimator) and Empirical Bayes approaches through Monte Carlo simulations. The mean square error was used as the test criterion for comparing the methods for point estimation; the smallest value indicates the best performing method with the estimated parameter value closest to the true parameter value. Coverage Probabilities (CPs) and average lengths (ALs) were obtained to evaluate the performances of the methods for constructing confidence intervals. The results reveal that the Bayesian approaches were excellent for point estimation when the true parameter value was small (0.5, 1 and 2). In the credible interval comparison, these methods obtained CP values close to the nominal 0.95 confidence level and the smallest ALs for large sample sizes (50 and 100), when the true parameter value was small (0.5, 1 and 2). Doi: 10.28991/esj-2021-01310 Full Text: PDF]]>
<![CDATA[Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis ]]>
<![CDATA[Abas Hasanovich Lampezhev]]>;
<![CDATA[Elena Yur`evna Linskaya]]>;
<![CDATA[Aslan Adal`bievich Tatarkanov]]>;
<![CDATA[Islam Alexandrovich Alexandrov]]>
<![CDATA[ Emerging Science Journal Vol 5, No 5 (2021): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01305
<![CDATA[This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PDF]]>
<![CDATA[GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic ]]>
<![CDATA[Harya Widiputra]]>
<![CDATA[ Emerging Science Journal Vol 5, No 5 (2021): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01300
<![CDATA[The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF]]>
<![CDATA[Development and Prototyping of Jet Systems for Advanced Turbomachinery with Mesh Rotor ]]>
<![CDATA[Yuri Appolonievich Sazonov]]>;
<![CDATA[Mikhail Albertovich Mokhov]]>;
<![CDATA[Inna Vladimirovna Gryaznova]]>;
<![CDATA[Victoria Vasilievna Voronova]]>;
<![CDATA[Khoren Arturovich Tumanyan]]>;
<![CDATA[Mikhail Alexandrovich Frankov]]>;
<![CDATA[Nikolay Nikolaevich Balaka]]>
<![CDATA[ Emerging Science Journal Vol 5, No 5 (2021): October ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01311
<![CDATA[This article presents the research results that aim to develop promising mesh turbomachines equipped with jet control systems. The turbomachines operating in difficult conditions in oil and gas production are mainly considered. At the same time, some research results can be used in other production branches, including power engineering and transport. Three-dimensional models for computer simulation of net turbines and jet control systems were developed. Prototypes and micromodels were created to test the performance of mesh turbines and jet control systems using additive technologies. A methodological approach is proposed to create a classification of jet control systems considering their design and technological features. In the course of numerical experiments, the extreme conditions of fluid and gas outflow through a nozzle equipped with a velocity vector control system, in the control range of adjustment of the velocity vector deflection angle from + 90o to -90o within a geometric hemisphere, have been considered for the first time. It was also shown that when using a dual-channel nozzle, there are possibilities to adjust the velocity vector angle (thrust vector) in the range of + 180o to -180owithin the geometric sphere. Compared with the known variants, the control range of the velocity vector angle is increased by nine times. These calculated data are presented in addition to the previously published results of physical laboratory experiments. Preliminary results of numerical experiments show the possibility of creating a new theory in the field of mesh turbines and jet systems. Patents support the novelty of the developed technical solutions. Doi: 10.28991/esj-2021-01311 Full Text: PDF]]>
<![CDATA[Analytical Study of Bending Characteristics of an Elastic Rectangular Plate using Direct Variational Energy Approach with Trigonometric Function ]]>
<![CDATA[F. C. Onyeka]]>;
<![CDATA[B. O. Mama]]>
<![CDATA[ Emerging Science Journal Vol 5, No 6 (2021): December ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01320
<![CDATA[In this paper, an analytical three-dimensional (3D) bending characteristic of an isotropic rectangular thick plate with all edges simply supported (SSSS) and carrying uniformly distributed transverse load using the energy technique is presented. The three-dimensional constitutive relations which involves six stress components were used in the established, refined shear deformation theory to obtain a total potential energy functional. This theory obviates application of the shear correction factors for the solution to the problem. The governing equation of a thick plate was obtained by minimizing the total potential energy functional with respect to the out of plane displacement. The deflection functions which are in form of trigonometric were obtained as the solution of the governing equation. These deflection functions which are the product of the coefficient of deflection and shape function of the plate were substituted back into the energy functional, thereafter a realistic formula for calculating the deflection and stresses were obtained through minimizations with respect to the rotations and deflection coefficients. The values of the deflections and stresses obtained herein were tabulated and compared with those of previous 3D plate theory, refined plate theories and, classical plate theory (CPT) accordingly. It was observed that the result obtained herein varied more with those of CPT and RPT by 25.39% and 21.09% for all span-to-thickness ratios respectively. Meanwhile, the recorded percentage differences are as close as 7.17% for all span-to-thickness ratios, when compared with three dimensional plate analysis. This showed that exact 3D plate theory is more reliable than the shear deformation theory which are quite coarse for thick plate analysis. Doi: 10.28991/esj-2021-01320 Full Text: PDF]]>
<![CDATA[Cellulose Microfiber Encapsulated Probiotic: Viability, Acid and Bile Tolerance during Storage at Different Temperature ]]>
<![CDATA[Usman Pato]]>;
<![CDATA[Dewi Fortuna Ayu]]>;
<![CDATA[Emma Riftyan]]>;
<![CDATA[Fajar Restuhadi]]>;
<![CDATA[Wasisso Tunggul Pawenang]]>;
<![CDATA[Royyan Firdaus]]>;
<![CDATA[Annisa Rahma]]>;
<![CDATA[Irwandi Jaswir]]>
<![CDATA[ Emerging Science Journal Vol 6, No 1 (2022): February ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2022-06-01-08
<![CDATA[This work aimed to analyze the physicochemical properties of cellulose from OPT used in the fabrication of CMF and evaluate the efficacy of the hydrogel CMF as an encapsulant for L. fermentum InaCC B1295 stored at room temperature and in the refrigerator. The Kjeldahl method was used to evaluate the protein content; the gravimetric method was used to determine OPT's ash, moisture, and fiber contents; the Soxhlet method was used to determine the fat content carbohydrates were computed using the difference method. The levels of holocellulose, lignin, and cellulose were also determined. Viability, acid and bile resistance of strain B1295 were evaluated at various temperatures for 35 days. The most abundant component of OPT fiber was cellulose, followed by hemicellulose and lignin. XRD examination revealed that OPT cellulose has a crystal index of 83.40%. FTIR analysis was used to detect the stretching vibrations of the –OH group on cellulose at 3419.03 cm-1. CMF hydrogel from OPT sustained L. fermentum InaCC B1295 survival for up to 28 days at room and refrigerated temperatures. At acidic conditions and in the presence of bile, the viability of L. fermentum InaCC B1295 was excellent, with a drop in cell population of less than 0.2 log CFU/g over 35 days at room and refrigerated temperatures. CMF obtained from OPT can be used as an encapsulant to maintain viability, acid resistance and bile of probiotics. There is still a need for research into the usage of CMF from OPT in combination with other encapsulants to extend the storage life of L. fermentum InaCC B1295. Doi: 10.28991/ESJ-2022-06-01-08 Full Text: PDF]]>
<![CDATA[The Role of Viral Marketing in Social Media on Brand Recognition and Preference ]]>
<![CDATA[Wilert Puriwat]]>;
<![CDATA[Suchart Tripopsakul]]>
<![CDATA[ Emerging Science Journal Vol 5, No 6 (2021): December ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/esj-2021-01315
<![CDATA[Viral marketing is one of the most effective and imperative marketing strategies. The prominence of digital technology and social media has elevated the importance of viral marketing campaigns by increasing their cost efficiency and enabling them to reach targeted audiences rapidly. This study aimed to examine the influence of viral marketing strategies on brand recognition and brand preference by developing a framework for the effectiveness of viral marketing (7I’s) in social media contexts and testing the associations among the 7I’s, brand recognition and brand preference. A quantitative research method with a structured questionnaire as the research tool was employed to collect data from a total of 286 respondents in Thailand. Structural equation modelling (SEM) was utilized to test the proposed hypotheses. The results showed that effective viral marketing relates positively to brand recognition (b = 0.440) and preference (b = 0.298). The mediation analysis also revealed that brand recognition partially mediates the relationship between effective viral marketing and brand preference. In terms of the moderating effects, the results indicated a stronger influence for effective viral marketing on brand preference among younger respondents (b = 0.336) than among older respondents (b = 0.278). This research makes a significant contribution to the existing literature by validating a theory-driven framework based on the novel concept of the 7I’s and its potential effect on customers’ brand perceptions. Doi: 10.28991/esj-2021-01315 Full Text: PDF]]>
<![CDATA[Violations at the Reference Point of Discontinuity: Limitations of Prospect Theory and an Alternative Model of Risk Choices ]]>
<![CDATA[Aaron Anil Chadee]]>;
<![CDATA[Xsitaaz T. Chadee]]>;
<![CDATA[Clyde Chadee]]>;
<![CDATA[Festus Otuloge]]>
<![CDATA[ Emerging Science Journal Vol 6, No 1 (2022): February ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2022-06-01-03
<![CDATA[The tilted S-shaped utility function proposed in Prospect Theory (PT) relied fundamentally on the geometrical notion that there is a discontinuity between gains and losses, and that individual preferences change relative to a reference point. This results in PT having three distinct parameters; concavity, convexity and the reference point represented as a disjoint between the concavity and convexity sections of the curve. The objective of this paper is to examine the geometrical violations of PT at the zero point of reference. This qualitative study adopted a theoretical review of PT and Markowitz’s triply inflected value function concept to unravel methodological assumptions which were not fully addressed by either PT or cumulative PT. Our findings suggest a need to account for continuity and to resolve this violation of PT at the reference point. In so doing, an alternative preference transition theory, was proposed as a solution that includes a phase change space to cojoin these three separate parameters into one continuous nonlinear model. This novel conceptual model adds new knowledge of risk and uncertainty in decision making. Through a better understanding of an individual’s reference point in decision making behaviour, we add to contemporary debate by complementing empirical studies and harmonizing research in this field. Doi: 10.28991/ESJ-2022-06-01-03 Full Text: PDF]]>
