Mathematical Learning Approach Research Articles

Optimization of porosity behavior of hybrid reinforced titanium metal matrix composite through RSM, ANN, and GA for multi-objective parameters

Titanium matrix composites (TMCs) have high specific strength and stiffness, and high-temperature TMCs can reduce weight by up to 50% when compared with monolithic super alloys while preserving equal stiffness and strength in jet engine systems for propulsion. The purpose of this work examines the use of mathematical models and learning approaches to optimize response such as porosity and control variables in synthesized hybrid titanium metal matrix composites (HTMMCs) reinforced by B4C-SiC-MoS2-ZrO2. To further understand the impacts of process factors on porosity reduction, the study employs methodologies such as the response surface methodology (RSM), integrated artificial neural networks (ANN), and genetic algorithm (GA). The findings indicate that these strategies have the potential to contribute to the industry. The optimal combination of 7.5wt.% SiC, 7.5wt.% B4C, 7.5wt.% ZrO2, 4wt.% MoS2, and 73.5wt.% Ti compositions was determined utilizing process factors such as milling period (6h), compaction pressure (50MPa), compact duration (50min), sintering temperature (1200°C), and sintering time (2h) as compared to pure Ti grade 5. The mechanical properties of the optimum combination of reinforcement weight percentage and process parameters resulted in a minimum porosity of 0.118%, density of 4.36gcm3, and micro-hardness of 63.4HRC boosted by 1.76%, and compressive strength of 2500MPa increased by 2.6%. In addition, these HTMMCs had a minimal wear rate of 0.176mm3/Nm and a corrosion resistance rate of 2.15×10-4mmpy. The investigation result analysis discovered that the RSM and combined ANN-GA models considerably enhanced the forecasting of multidimensional interaction difficulties in composite material production that were highly statistically connected, with R2 values of 0.9552 and 0.97984. The ANN-GA model provided a 95% confidence range for porosity predictions, which increased the production use of titanium-based particle composites. Furthermore, HMMCs can be utilized in the automotive and aviation industries with enhanced corrosion and wear resistance.

A review on agrowaste based activated carbons for pollutant removal in wastewater systems

Environmental pollution from chemicals utilized in manufacturing, pharmaceuticals, and chemical process industries is of serious concern nowadays due to the contamination that ensues when these chemicals are discharged into water bodies. Activated carbon adsorption provides an efficient and economically viable means for mitigation of toxic chemicals (i.e., heavy metals, dyes, pharmaceutics, and antibiotics). However, the exorbitant cost of commercial activated carbons has resulted in the search for low-cost alternatives for the treatment of contaminated effluents. An exhaustive literature survey in this area is necessary to know the extent of work done in this area and seek out the gaps that future research will provide answers to. In this review, various works on activated carbon utilization, batch adsorption, fixed-bed adsorption (experimental and numerical studies) are summarized. This review elucidates the different kinetic and isotherm models of agrowastes-derived activated carbon materials in context with pollutants (dyes, heavy metals, pharmaceuticals, miscellaneous adsorbates) removal through batch and column methods. In addition, fixed-bed column adsorption/regeneration methods using various activated carbons derived from agrowastes are discussed. Among these methods, heavy metal adsorption from aqueous solutions by the activated carbons is the most efficient. The deployment of mathematical and machine learning approaches (ANN and novel GMDH algorithms) in optimization of batch and continuous adsorption processes are also highlighted. Numerical simulation of fixed-column adsorption systems for more improved industrial-scale column designs is described. Conclusions and future challenges of chemicals removal from polluted wastewater utilizing agrowaste-derived activated carbons are also presented.

High School Students' Mathematical Communication Skills Through Ethnomatematics-Based Realistic Mathematics Learning Approaches in Geometry Transformation Materials

Mathematical communication ability is the ability of students to use mathematics as a form of communication (the language of mathematics) and student's ability to convey the mathematics they learn as the content of the message to be conveyed. This study aims to describe the Mathematical Communication Skills of High School Students Through an Ethnomatematics-Based Realistic Mathematics Learning Approach on Geometry Transformation Materials. This research is a type of descriptive research with a qualitative approach. The selection of subjects used in this study used an interpersonal intelligence questionnaire, which involved all students of class XI IIS 3 State Islamic High School 1 Pasuruan. Based on the interpersonal intelligence questionnaire scores, three subjects were selected with high, medium, and low levels of interpersonal intelligence. Then, these three subjects will examine the student's ability to draw the correct solution, explain the answer systematically, and construct a problem into a mathematical model. The results of this study indicate that the mathematical communication skills of students with high, medium, and low levels of interpersonal intelligence have many differences in problem-solving on the questions given

Ethnomathematics Exploration on Typical Angkola Woven Patterns, Sipirok, South Tapanuli, North Sumatra, Indonesia

This research explores the application of mathematical elements found in certain cultures. The object that is the focus of this research is the woven fabric typical of the Angkola ethnic group, which is part of the cultural heritage of the Angkola tribe in South Tapanuli district, North Sumatra province. Descriptive qualitative is the research model used in this study and ethnography is the research approach used, focusing on ethnomathematics in the motifs of Angkola's typical woven fabrics. The summary result of the research is that there is an application of mathematical concepts in this cultural heritage object, namely the basic concept of flat geometric shapes, the applied concept of geometric transformation, and the concept of sets. With this research it is hoped that it can contribute to new ethnomathematics research references and can be used as a reference for new mathematical category learning approaches, especially for the province of North Sumatra.

Development of Fraction Material Mathematics Module with Realistic Mathematics Education Learning Approach

This research aims to determine the steps of module development and the level of mathematics module fourth grade with realistic mathematics education learning approach quality. This research includes Research and Development (RnD) with the ADDIE development model. The object and subject in this research is the understanding of grade IV faction material in SD Negeri 2 Riau Silip. The instruments used are speech guidelines, expert assessments, and teacher responses, and student responses. The data analysis technique used qualitative and quantitative data. Qualitative data was analyzed from the results of testing the quality and validity of the module. The results of the research contain development steps, namely analysis stages, resistance to pressure, and resistance to development. The results of the assessments of media experts, material experts, learning experts and language experts obtained the following values of 85.3, 90.7, 80, and 80. While the results of the responses of fourth grade teachers and students were 100 and 92.8, the average assessment and expert validation test and the response of class IV teachers and students was 88.13 with the "Very Good" category. Based on the results of the assessments from the experts, the fourth grade teacher's response, and the student's response it was stated that the fourth grade mathematics modui fraction material with a realistic mathematical education learning approach was included in the "Very Good" category so that it was suitable for use in the classroom learning process.

Penerapan Data Mining Untuk Menentukan Jenis Asuransi Yang Paling Diminati Di Pt Aia Financial Dengan Metode Clustering

Finding information that is concealed in databases is referred to as data mining. Data mining is a process used to extract and find relevant information and related knowledge from numerous very big datasets using statistical, mathematical, artificial intelligence, and machine learning approaches. The steps involved in the data mining process start with the selection of data from the source data to the target data, followed by preprocessing to enhance the quality of the data, transformation, data mining, and stages of interpretation and assessment that result in new information. Organizing data sets into many groups through the process of clustering ensures that objects in one group share a lot in common and differ greatly from those in other groups. Data mining technique K-Means Clustering divides data into one or more clusters. RapidMiner is the program used in this data mining application, which uses the Knowledge Discovery in Databases (KDD) stage. According to this report, AIA Family First Protection, AIA Prolink, and AIA Life Secure are the three most popular insurance products. With the use of this research, PT. AIA Financial will be able to identify the most popular insurance product categories and market these products to the general public.

Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates.

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.

The Effect of Mathematics Learning Approaches and Self-Regulation to Recognize the Concept of Early Numbers Ability

The ability to recognize the concept of early numbers in early childhood is very important to develop so that children are ready to take part in learning mathematics at a higher level. This study aims to determine the effect of mathematics learning approaches and self-regulation to recognize the concept of early numbers ability in kindergarten. The study used an experimental method with a treatment design by level 2x2. The sample used was 32 children. Score data, ability to recognize number concepts, analyzed and interpreted. The results showed that: (1) The Realistic Mathematics Education approach is better than the Open Ended Approach in improving the ability to recognize children's number concepts; (2) There is an interaction effect between mathematics learning approaches and Self-Regulation to recognize the concept of early numbers ability; (3) The Realistic Mathematics Education approach is more suitable for children with high self-regulation, (4) The Open Ended approach is more suitable for children with low self-regulation. Subsequent experiments are expected to find mathematics learning approaches for children whose self-regulation is low on recognizing the concept of early numbers ability.
 Keywords: mathematics learning approach, self-regulation, early number concept ability

Analysis Of Mathematics Learning Media Based on Realistic Mathematics Learning Approaches to Improve Mathematical Problem-Solving Ability

This study aims to analyze the results of mathematics learning media based on a mathematics learning approach and to improve the ability to solve mathematical problems. This type of research is qualitative research using the literature study method, so the data obtained from this research is secondary data derived from collections of literature such as journals that are relevant to the formulation of the research problem. Data analysis techniques by way of content analysis and explanatory analysis, namely, analyzing, describing and concluding the findings of the literature taken. The results of this study indicate that, (1) the results of the analysis of realistic mathematics learning media have practical, valid and effective qualities for improving mathematical problem solving abilities, (2) students' mathematical problem solving abilities after being analyzed by effective learning media.

Quasiconformal model with CNN features for large deformation image registration

<p style='text-indent:20px;'>Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling. In recent years, there has been a surge of interest in the use of machine learning for image registration. In this paper, we develop a novel method for large deformation image registration by a fusion of quasiconformal theory and convolutional neural network (CNN). More specifically, we propose a quasiconformal energy model with a novel fidelity term that incorporates the features extracted using a pre-trained CNN, thereby allowing us to obtain meaningful registration results without any guidance of prescribed landmarks. Moreover, unlike many prior image registration methods, the bijectivity of our method is guaranteed by quasiconformal theory. Experimental results are presented to demonstrate the effectiveness of the proposed method. More broadly, our work sheds light on how rigorous mathematical theories and practical machine learning approaches can be integrated for developing computational methods with improved performance.</p>

An Empirical Investigation of Mathematics Learning Approaches and Metacognitive Awareness of Students

The goal of this study is to examine the mathematics learning approaches and metacognitive awareness of 6th, 7th and 8th grade students with regard to some variables. This is a quantitative study conducted in a survey model. In the 2018-2019 academic year, the working group was secondary school students in the public schools in Central Anatolia region 6.7.8. It consists of 330 (166 girls, 164 boys) students studying at the grade level. As the data collection tools, Göktepe-Yıldız and Özdemir’s (2018) “Mathematics Learning Approach Scale”, which was originally developed by Schraw and Dennison (1994); The Metacognitional Awareness Inventory developed by Akın, Abacı and Çetin (2007) was utilized. According to the empirical findings, for sixth, seventh, and eighth grade students, scores for deep and strategic learning approaches are above the medium level, whereas scores for surface approach is below the medium level. In addition, it has been determined that mathematics learning approaches differ significantly according to gender, grade level and mathematics achievement. As a consequence of the analysis, it was seen that students' levels of metacognitive awareness were high. The levels of metacognitive awareness did not differ by gender, but differed significantly by grade level and mathematics achievement. In addition, while there exists a positive and statistically significant relationship between deep learning and strategic learning approaches and metacognitive awareness no statistically significant relationship was found between the surface learning approach and the metacognitive awareness.

The influence of science, technology, engineering, and mathematics (STEM) learning approaches on learning outcomes

The objective of the study is to discover the effect of the Science, Technology, Engineering, and Mathematics (STEM) learning approach on the physics learning outcomes The study is considered as quasi-experimental research by implementing the non-equivalent, pretest-posttest as the research design. There were two science classes of SMA Negeri 1 Sindue Tobata were selected as the sample of the study. The Science, Technology, Engineering, and Mathematics learning approach was applied to the experimental class while the scientific approach was applied to the control class. The learning outcomes test obtained showed that the average score of the experimental group was 15.95 and the average score of the control group was 12.25. Based on the results of hypothesis testing, obtained that tcount=3.00 and t_(0.0975(38))=2.024 at a significance level of a=0.05. It means that the value of t_count is outside the acceptance area of H_0. The findings concluded that there were differences in physics learning outcomes between the students who were taught by using STEM approach and the students who were only taught by implementing the scientific approach in SMA Negeri 1 Sindue Tobata.

Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder

Due to the increase of lung cancer globally, and particularly in Korea, survival analysis for this type of cancer has gained prominence in recent years. For this task, mathematical and traditional machine learning approaches are commonly used by medical doctors. While the deep learning approach has had proven success in computer vision tasks, natural language processing and other AI techniques are also adopted for this task. Due to the privacy issues and management process, data in medicine are difficult to collect, which leads to a paucity of samples. The small number of samples makes it difficult to use deep learning and renders this approach unusable. In this investigation, we propose a network architecture that combines a variational autoencoder (VAE) with the typical DNN architecture to solve the survival analysis task. With a training size of n = 4107, MVAESA achieves a C-index of 0.722 while CoxCC, CoxPH, and CoxTime achieved scores of 0.713, 0.703, and 0.710, respectively. With a small training size of n = 379, MVAESA achieves a C-index of 0.707, compared with 0.689, 0.688 and 0.690 for CoxCC, CoxPH, and CoxTime, respectively. The results show that the combination of a VAE with a target task makes the network more stable and that the network could be trained using a small-sized sample.

REGRESSION ANALYSIS FOR THE ADSORPTION OF OIL USING HUMAN HAIR AS NATURAL ADSORBENT

The abundance of human hair waste (known as keratin biomass) in most parts of the world and its accumulation in waste streams are causing many environmental problems. Human hair’s potential as a natural adsorbent to remove oil from wastewater was emphasised in this study. Mathematical and deep learning approaches were adopted to develop the regression models of oil adsorption using gents and ladies’ hair wastes. The experimental results were obtained from literature review to perform regression analysis using Artificial Neural Network (ANN) in Matlab, Microsoft Excel, and Design Expert 6.0.6. The efficiency of these tools was compared in predicting the oil removal percentage within a specified range of adsorption parameters. This was done by comparing the R2 value of the established adsorption models. In this study, the effect of different adsorption parameters, namely pH, contact time, and adsorbent dosage, on the oil removal percentage was included in the regression analysis. Subsequently, using the regression model with the highest R2 valuefor both gents and ladies’ hair adsorbent, a graphical user interface (GUI) was developed for the oil adsorption process to ease users in predicting the oil removal percentage within the specified range of adsorption parameters. The results showed that the adsorption model with the highest R2 value of 0.9570 for gents hair and 0.9650 for ladies hair was developed using the ANN tool, implying its superiority in correlating the adsorption parameters to the oil removal percentage. However, in creating the GUI layout for the oil adsorption process, the regression model generated by the RSM regression tool (the second-highest R2 at 0.8290 for gents hair and 0.6158 for ladies hair) was adopted to the limitations in retrieving the regression model from the ANN tool. The outcome of this study is expected to benefit users without prior knowledge of the oil adsorption process using human hair adsorbent to predict the removal percentage within a specified range of adsorption parameters, without the hassle of conducting experimental work.
 Keywords: Human hair waste, keratin biomass, regression, oil adsorption, graphical user interface

A Comparison of Mathematics Learning Approaches of Gifted and Non-Gifted Students

In this study, it was aimed to investigate the mathematics learning approaches of gifted students and successful students who were not diagnosed as gifted in terms of variables of giftedness, gender, grade level, parents’ profession and education level. Descriptive research model, which is one of the general survey models, was used in the study. For the 2018-2019 academic years, a total of 239 6th, 7th and 8th grade students, 84 of whom were gifted students and 155 of whom were non-gifted students, participated into the study. Data on gifted students were collected from the Science and Arts Center of a province in the Central Anatolia region, while data on non-gifted students were collected from a secondary school in the same province. Students' mathematics learning approaches were determined by using the “Scale of Mathematics Learning Approaches”. When the findings were examined, a significant differentiation was observed in favor of gifted students in the in-depth learning approach between gifted and non-gifted students. In addition, there is a significant difference in favor of female students in in-depth and strategic learning sub-dimensions among gifted students. When the mathematics learning approaches of gifted and non-gifted students were compared according to grade level, no significant difference was found in the 8th grade level average scores.

The Relationship between Sixth Grade Students’ Mathematics Learning Approaches and Perceived Teacher Constructive Affective Support and Mathematics Achievements

Bu çalışmanın temel amacı, altıncı sınıf öğrencilerinin matematik öğrenme yaklaşımları, algılanan öğretmen yapıcı duygusal destek ve matematik başarıları arasındaki ilişkiyi belirlemektir. Araştırmaya 416 altıncı sınıf öğrencisi katılmıştır. Araştırmaya katılan öğrencilerin %54.3’ü (n=226) kız, %45.7’si (n=190) erkektir. Çalışmada veri toplama aracı olarak, Algılanan Öğretmen Yapıcı Duygusal Destek ile Matematik Öğrenme Yaklaşımları ölçekleri kullanılmıştır. Öğrencilerin matematik başarılarını belirlemek için dönem sonu matematik not ortalamaları kullanılmıştır. Verilerin analizinde, Pearson momentler çarpımı korelâsyon katsayısı ve çoklu doğrusal regresyon kullanılmıştır. Elde edilen bulgulara göre, öğrencilerin matematik başarıları ile derinlemesine öğrenme yaklaşımı, yüzeysel öğrenme yaklaşımı, stratejik öğrenme yaklaşımı ve algılanan öğretmen yapıcı duygusal destek arasında anlamlı ilişkiler tespit edilmiştir. Çoklu regresyon analiz sonuçlarına göre, matematik öğrenme yaklaşımları ile algılanan öğretmen yapıcı duygusal destek matematik başarısındaki varyansın %26’sını açıklamıştır. Aynı zamanda algılanan öğretmen duygusal destek ile öğrenme yaklaşımları matematik başarısının anlamlı yordayıcıları olmuştur. Elde edilen bulgular ışığında birtakım önerilerde bulunulmuştur.

Designing learning trajectory of circle using the context of Ferris wheel

Ferris wheel is one amusement playground that resembles a giant spinning wheel. Many students are familiar with the Ferris wheel in the mini version of it at night market festivals. This is the potential for learning mathematics. Furthermore, there is a mathematical learning approach called Indonesian Realistic Mathematics Education (IRME) where students learn with contexts which are close to students' life as starting points. Therefore, this study aims to design a learning trajectory using the IRME approach with the Ferris wheel as the context in the learning process to support students' understanding of the learning about circles. The research method is design research that consists of three stages: preliminary design, design experiments, and retrospective analysis. The subjects were 20 eighth-grade students from one of the private Junior High School in Yogyakarta. The instruments used are videos to see the learning process and when students work on the given problems, photos to refer the results of student work, and written test in worksheets to get the data on student's work. The research result explores the learning trajectory practiced using the Ferris wheel as the context seen in the student's daily activities. The learning trajectory consists of four events, namely assembling the Ferris wheel, drawing an illustration of the Ferris wheel, making a list of the circle parts, and solving a problem related to the parts of the circle. Lastly, this study shows that learning trajectory activities have essential roles in supporting students' understanding of the concept of a circle.

Desain LKS Berbasis Karakter Komunikasi Matematis dan Kemandirian dengan Pendekatan PMR

This research is based on learning mathematics which is generally centered on teachers, who are considered able to make students understand the material provided. Whereas, in the implementation of curriculum 2013 students should play an active role in learning. The techniques used in this research are observations, interviews, polls, tests and documentation. The learning Model in this study uses a realistic mathematical learning approach. The purpose of this research is to design the LKS with a realistic mathematical approach that is capable of developing mathematical and self-reliance communication characters in students. The research instruments used are interview guidelines, observation guidelines, LKS, expert assessment sheets, teacher and student response polls. The results showed that the LKS developed were said to be worthy of use based on the 4.2 and 3.75 scores of material experts and media experts. As well as scores 26.3 and 75.97 of students and teachers who show interesting LKS. So it concludes that the LKS are worth using and attracting teachers and students ' attention.

Analysis of Thinking Ability Intuition of Mathematical Education Study Program Students Based on Realistic Mathematical Approaches

Intuitive is a process of thinking based on intuition that is a purification of conclusions that are strongly reasoned through feeling and indispensable in learning mathematics. The research objectives were: describe the ability of mathematical intuition thinking (Kb-iM) and the effectiveness of the application of realistic mathematical approaches from students of Mathematics Education Study Program FMIPA Medan State University. The sample of the study was 36 students in the 2015 class who were / had attended the Kapita Selekta Mathematics School course. The research instrument was in the form of 8 items of objective test of mathematical intuitive thinking ability. This test was developed based on the formulation of indicators that are built from supporting theories. The results of data analysis show that: (1) for the pre-test Kb-iM the range = 0.88; smallest score 2.38; biggest score of 3.25; average = 2.86 and standard deviation = 0.24. The same thing with the Kb-iM post-test obtained a range of values = 1.30; smallest score 2.60; biggest score 3.9; average = 3.24 and standard deviation = 0.31. Average values mean that the level of initial achievement of students’ mathematical intuitive thinking skills was only 71.5% and after realistic mathematics learning it had reached 81.0% or an increase of 10%. This fact gave the meaning that realistic mathematical learning approaches play a positive role to improve students’ mathematical intuitive thinking skills. At the end of the study it turned out that the level of mathematical intuitive thinking ability of students was at least at the moderate level.

Penerapan ELPSA Framework Berbantuan Game pada Materi Eksponensial

The Application of the ELPSA Framework Assisted by Game in Teaching Exponential. There is still a tendency for students to avoid mathematical assignments is a challenge for educators. Therefore, to present interesting and quality mathematics learning, one of them can be through the ELPSA framework. ELPSA framework is a mathematical learning approach that contains five components, namely experience, language, pictorial, symbolic, and application. The game was chosen to present training as a part of symbolic component that attract students. This study aims to determine the mastery of student learning through the application of the ELPSA framework assisted by games on exponential material, student responses to the learning activity, and the dynamics of students' emotional activity in game utilization. This research use mixed method with convergent parallel mixed method design. The instruments used were learning outcomes tests, student response questionnaires, and observation sheets of student emotional activity. The study population was all students of MTsN 1 Aceh Tengah, and the sample was 17 students of class VIII-1. Based on the results of data processing using the t test, it can be concluded that the value of students reaches completeness. In addition, students respond well to learning and the use of games. Observation of the emotional activity of students during play shows the seriousness is dominating the emotional activity that arises, followed by pleasure, and curiosity. Whereas signs of boredom and disappointment do not arise. This study presents alternative mathematical learning designs to achieve learning goals, as well as attracting students.