bluuebunny/crossref_metadata_2025 huggingface dataset:
Termination points and homoclinic glueing for a class of inhomogeneous nonlinear ordinary differential equations
Authors: Keeler J S , Blyth M G , King J R
Published: January 2021

Solutions u(x) to the class of inhomogeneous nonlinear ordinary differential equations taking the form

u

″

+

u

2

=
α
f

(

x

)

for parameter α are studied. The problem is defined on the x line with decay of both the solution u(x) and the imposed forcing f(x) as |x| → ∞. The rate of decay of f(x) is important and has a strong influence on the structure of the solution space. Three particular forcings are examined primarily: a rectilinear top-hat, a Gaussian, and a Lorentzian, the latter two exhibiting exponential and algebraic decay, respectively, for large x. The problem for the top hat can be solved exactly, but for the Gaussian and the Lorentzian it must be computed numerically in general. Calculations suggest that an infinite number of solution branches exist in each case. For the top-hat and the Gaussian the solution branches terminate at a discrete set of α values starting from zero. A general asymptotic description of the solutions near to a termination point is constructed that also provides information on the existence of local fold behaviour. The solution branches for the Lorentzian forcing do not terminate in general. For large α the asymptotic analysis of Keeler, Binder and Blyth (2018 J. Fluid Mech.
832 73–96) is extended to describe the behaviour on any given solution branch using a method for glueing homoclinic connections.
Students’ understanding of gravity using the rubber sheet analogy: an Italian experience
Authors: Postiglione A , De Angelis I
Published: January 2021

General relativity (GR) represents the most recent theory of gravity, on which all modern astrophysics is based, including some of the most astonishing results of physics research. Nevertheless, its study is limited to university courses, while being ignored at high-school level. To introduce GR in high school, one of the approaches that can be used is the so-called rubber sheet analogy (RSA), i.e. comparing space-time to a rubber sheet that deforms under a weight. In this paper, we analyse the efficacy of an activity for high-school students held at the Department of Mathematics and Physics of Roma Tre University that adopts the RSA to address several topics related to gravity. We present the results of the questionnaires we administered to over 150 Italian high-school students to investigate their understanding of the topics treated.
Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty
Authors: van der Meer Marjolein C , Bosman Peter A N , Niatsetski Yury , Alderliesten Tanja , Pieters Bradley R , Bel Arjan
Published: January 2021

Purpose. Recently, we introduced a bi-objective optimization approach based on dose-volume indices to automatically create clinically good HDR prostate brachytherapy plans. To calculate dose-volume indices, a reconstruction algorithm is used to determine the 3D organ shape from 2D contours, inevitably containing settings that influence the result. We augment the optimization approach to quickly find plans that are robust to differences in 3D reconstruction. Methods. Studied reconstruction settings were: interpolation between delineated organ contours, overlap between contours, and organ shape at the top and bottom contour. Two options for each setting yields 8 possible 3D organ reconstructions per patient, over which the robust model defines minimax optimization. For the original model, settings were based on our treatment planning system. Both models were tested on data of 26 patients and compared by re-evaluating selected optimized plans both in the original model (1 organ reconstruction, the difference determines the cost), and in the robust model (8 organ reconstructions, the difference determines the benefit). Results. Robust optimization increased the run time from 3 to 6 min. The median cost for robust optimization as observed in the original model was −0.25% in the dose-volume indices with a range of [−0.01%, −1.03%]. The median benefit of robust optimization as observed in the robust model was 0.93% with a range of [0.19%, 4.16%]. For 4 patients, selected plans that appeared good when optimized in the original model, violated the clinical protocol with more than 1% when considering different settings. This was not the case for robustly optimized plans. Conclusions. Plans of high quality, irrespective of 3D organ reconstruction settings, can be obtained using our robust optimization approach. With its limited effect on total runtime, our approach therefore offers a way to account for dosimetry uncertainties that result from choices in organ reconstruction settings that is viable in clinical practice.
A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks
Authors: Nguyen Dan , Sadeghnejad Barkousaraie Azar , Bohara Gyanendra , Balagopal Anjali , McBeth Rafe , Lin Mu-Han , Jiang Steve
Published: January 2021

Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinicians is not whether the model is accurate, but whether the model can express to a human operator when it does not know if its answer is correct. We propose to use Monte Carlo Dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning (DL) models to produce uncertainty estimations for radiation therapy dose prediction. We show that both models are capable of generating a reasonable uncertainty map, and, with our proposed scaling technique, creating interpretable uncertainties and bounds on the prediction and any relevant metrics. Performance-wise, bagging provides statistically significant reduced loss value and errors in most of the metrics investigated in this study. The addition of bagging was able to further reduce errors by another 0.34% for

D

m
e
a
n

and 0.19% for

D

max

,

on average, when compared to the baseline model. Overall, the bagging framework provided significantly lower mean absolute error (MAE) of 2.62, as opposed to the baseline model’s MAE of 2.87. The usefulness of bagging, from solely a performance standpoint, does highly depend on the problem and the acceptable predictive error, and its high upfront computational cost during training should be factored in to deciding whether it is advantageous to use it. In terms of deployment with uncertainty estimations turned on, both methods offer the same performance time of about 12 s. As an ensemble-based metaheuristic, bagging can be used with existing machine learning architectures to improve stability and performance, and MCDO can be applied to any DL models that have dropout as part of their architecture.
An end-to-end assessment on the accuracy of adaptive radiotherapy in an MR-linac
Authors: Axford A , Dikaios N , Roberts D A , Clark C H , Evans P M
Published: January 2021

Purpose. To develop and demonstrate an end-to-end assessment procedure for adaptive radiotherapy (ART) within an MR-guided system. Methods and materials. A 3D printed pelvic phantom was designed and constructed for use in this study. The phantom was put through the complete radiotherapy treatment chain, with planned internal changes made to model prostate translations and shape changes, allowing an investigation into three ART techniques commonly used. Absolute dosimetry measurements were made within the phantom using both gafchromic film and alanine. Comparisons between treatment planning system (TPS) calculations and measured dose values were made using the gamma evaluation with criteria of 3 mm/3% and 2 mm/2%. Results. Gamma analysis evaluations for each type of treatment plan adaptation investigated showed a very high agreement with pass rates for each experiment ranging from 98.10% to 99.70% and 92.60% to 97.55%, for criteria of 3%/3 mm and 2%/2 mm respectively. These pass rates were consistent for both shape and position changes. Alanine measurements further supported the results, showing an average difference of 1.98% from the TPS. Conclusion. The end-to-end assessment procedure provided demanding challenges for treatment plan adaptations to demonstrate the capabilities and achieved high consistency in all findings.
Wakefield decay in a radially bounded plasma due to formation of electron halo
Authors: Spitsyn R I , Lotov K V
Published: January 2021

There is a new effect that can limit the lifetime of a weakly non-linear wakefield in a radially bounded plasma. If the drive beam is narrow, some of the plasma electrons fall out of the collective motion and leave the plasma radially, forming a negatively charged halo around it. These electrons repeatedly return to the plasma under the action of the charge separation field, interact with the plasma wave and cause its damping. The lowest-energy halo electrons take the energy from the wave more efficiently, because their trajectories are bent by the plasma wave towards the regions of the strongest acceleration. For correct accounting of the wave damping in simulations, it is necessary to take the simulation window at least twice as wide as the plasma.
Dim and bright void regimes in capacitively-coupled RF complex plasmas
Authors: Pikalev A , Semenov I , Pustylnik M , Räth C , Thomas H
Published: January 2021

We demonstrate experimentally that the void in capacitively-coupled RF complex plasmas can exist in two qualitatively different regimes. The ‘bright’ void is characterized by bright plasma emission associated with the void, whereas the ‘dim’ void possesses no detectable emission feature. The transition from the dim to the bright regime occurs with an increase of the discharge power and has a discontinuous character. The discontinuity is manifested by a kink in the void size power dependencies. We reproduce the bright void (mechanically stabilized due to the balance of ion drag and electrostatic forces) by a simplified time-averaged 1D fluid model. To reproduce the dim void, we artificially include the radial ion diffusion into the continuity equation for ions, which allows to mechanically stabilize the void boundary due to very weak electrostatic forces. The electric field at the void boundary occurs to be so small that it, in accordance with the experimental observation, causes no void-related emission feature.
Computational intelligence of Levenberg-Marquardt backpropagation neural networks to study the dynamics of expanding/contracting cylinder for Cross magneto-nanofluid flow model
Authors: Shah Z , Raja M A Z , Chu Yu-Ming , A Khan W , Abbas S Z , Shoaib M , Irfan M
Published: January 2021

In the present investigation, design of integrated numerical computing through Levenberg-Marquardt backpropagation neural network (LMBNN) is presented to examine the fluid mechanics problems governing the dynamics of expanding and contracting cylinder for Cross magneto-nanofluid flow (ECCCMNF) model in the presence of time dependent non-uniform magnetic force and permeability of the cylinder. The original system model ECCCMNF in terms of PDEs is converted to nonlinear ODEs by introducing the similarity transformations. Reference dataset of the designed LMBNN methodology is formulated with Adam numerical technique for scenarios of ECCCMNF by variation of thermophoresis temperature ratio parameter, Brownian motion, suction parameters as well as Schmidt, Prandtl, local Weissenberg and Biot numbers. To calculate the approximate solution for ECCCMNF for different scenarios, the training, testing, and validation processes are conducted in parallel to adapt neural network by reducing the mean square error (MSE) function through Levenberg-Marquardt backpropagation. The comparative studies and performance analyses based on outcomes of MSE, error histograms, correlation and regression demonstrate the effectiveness of designed LMBNN technique.
Fabrication of a green and sensitive quantum dots based fluorescent probe for determination of sparfloxacin in biological samples and drug formulations
Authors: Muhammad Mian , Ara Behisht , Ali Faiz , Ullah Ikram
Published: January 2021

A simple and fast spectrofluorimetric method for determination of sparfloxacin (SPAR) based on fluorescence quenching of colloidal water soluble TGA caped, Mn doped ZnS quantum dots (QDs) has been described. The QDs having characteristic fluorescence spectra with maximum emission at 632 nm (Excitation at 315 nm) were characterized using Energy Dispersive x-ray and x-ray Diffraction techniques. The effect of various parameters such as concentration of QDs, time, pH, common excipients and metal ions on the quenching phenomenon was investigated. Fluorescence quenching was found to be maximum with 10 μg ml−1 of QDs at pH 7 with fluorescence intensity remaining constant upto 60 min. The Stern-Volmer calibration plot of F0/F as a function of sparfloxacin concentration was found to be linear in the range of 0.2–2.2 μg ml−1 with r2 = 0.995. Under optimal experimental conditions the method was found to be interference free. The percent relative standard deviation of the proposed method calculated against method blank was found to be 1.67%. The limit of detection and the limit of quantitation for SPAR (n = 3 and 10 respectively) were calculated to be 0.07 and 0.21 μg ml−1 respectively. Average percent recoveries (±SD) obtained were found to be 89.16 ± 0.03 to 94.44 ± 0.15 μg ml−1 for serum and 85.00 ± 0.10 to 97.77 ± 0.37 μg ml−1 for urine samples. In case of commercial formulations sparaxcin and spar, the maximum % recovery was found to 89.33 ± 0.54% while in case of Quspar the % recovery obtained was less than 71.83 ± 0.14%. The proposed method was statistically compared with reported chromatographic method and no significant difference was observed.
DBS electrode localization and rotational orientation detection using SQUID-based magnetoencephalography
Authors: Yalaz Mevlüt , Sohail Noor M , McIntyre Cameron C , Butz Markus , Schnitzler Alfons , Deuschl Günther , Höft Michael
Published: January 2021

Objective. The aim of the present study was to investigate the accuracy of localization and rotational orientation detection of a directional deep brain stimulation (DBS) electrode using a state-of-the-art magnetoencephalography (MEG) scanner. Approach. A directional DBS electrode along with its stimulator was integrated into a head phantom and placed inside the MEG sensor array. The electrode was comprised of six directional and two omnidirectional contacts. Measurements were performed while stimulating with different contacts and parameters in the phantom. Finite element modeling and fitting approach were used to compute electrode position and orientation. Main results. The electrode was localized with a mean accuracy of 2.2 mm while orientation was determined with a mean accuracy of 11∘. The limitation in detection accuracy was due to the lower measurement precision of the MEG system. Considering an ideal measurement condition, these values represent the lower bound of accuracy that can be achieved in patients. Significance. However, a future magnetic measuring system with higher precision will potentially detect location and orientation of a DBS electrode with an even greater accuracy.
Development of the Learning Instruction Based on Problem Based Learning Models Oriented with Mitigation of Mount Eruption and Lava Floods on the Mathematical Reasoning Ability of Class VIII Students of SMP / MTs
Authors: Badri R , Yerizon
Published: January 2021

During 2019, the National Disaster Management Agency (BNPB) noted that there had been 3622 disasters in Indonesia, one of which was natural disasters such as volcanic eruptions and cold lava floods. In terms of volcanoes, Indonesia is the most active volcanoes location in the world and it have many tectonic plates that have the potential to cause volcanic eruptions, tsunamis, and earthquakes. Therefore, the Indonesia Government recommends providing education on natural disaster mitigation in schools. The goal of this study was to determine the mathematical reasoning abilities of students and the knowledge and attitudes of students towards mitigating natural disasters. This research is a development research, the development model used in this research is the Plomp model which consists of three phases, namely preliminary research, prototyping phase, and assessment phase. The instruments used during the research were observation sheets, interview guides, questionnaires, validation sheets for learning devices, and tests of students' mathematical reasoning abilities. From this research, it is proven that about 71.4% of MTsN 6 Agam students stated that there has been no learning about natural disaster mitigation. Based on the results of the mathematical reasoning ability test, there was an increase to 72.7% from the initial test result of 25.47%. Natural disaster mitigation knowledge does not have to be provided to students in specific subjects, but can be integrated into existing subjects, one of which is mathematics.
The Development of Geometrical Learning Devices Based On Rumah Gadang Ethnomathematics for Grade VII Junior High School
Authors: Setiawan H , Fauzan A , Arnawa I M
Published: January 2021

Based on the observations, interviews and preliminary analysis conducted in several schools, it showed that the learning devices used so far have not been linked to the culture around the students. Besides, the students’ learning outcomes were also still low. This was caused by (1) the learning resources that were still general in nature and not yet associated with the students' daily lives, (2) the student worksheet (LKPD) used so far did not contain sufficient activities for learning mathematics, (3) the LKPD contained problems that were difficult for the students to imagine, and (4) the activities in the lesson plan did not show the activities that lead the students to be active and can find their own mathematical concepts. Based on these problems, the researcher developed the Rumah Gadang ethnomathemic-based geometrical learning devices in form of lesson plan (RPP) and LKPD. This study was a research and development conducted with the Plomp development model. The result of the study show that RPP and LKP was valid and practical.
The Development of Mathematics Learning Devices Based On Problem Based Learning and Geogebra-Assisted for Junior High School Students
Authors: Priyatno N , Arnawa I M , Bakar N N
Published: January 2021

Based on observations, interviews, and preliminary analysis conducted, it showed that students' mathematical problem-solving skills were still not satisfying. This was caused by the teaching materials used by teachers in teaching have not helped students a lot in understanding mathematical concepts and properties. Based on these problems, the main objective of this research is to produce a practical mathematics learning devices in the form of RPP and LKPD for junior high school students based on problem based learning (PBL) and geogbra-assisted. This study was research and development (R&D) conducted with the Plomp development model. The subjects of this study were the student's grade viii SMP Negeri 25 Padang. Based on the R & D that has been carried out, it obtained that the learning devices (RPP and LKPD) were valid and practical.
The Effect of Blended Learning on Student Learning Outcomes in Calculus Vector Course
Authors: Tasman F , Ahmad D , Rifandi R , Sari W W
Published: January 2021

Rapid development of technology has become an inseparable part of life. This poses a challenge for the world of education to be able to take advantage of technological developments in achieving educational goals. However, switching from a direct learning model to technology-based learning requires a lot of effort and money. However, this can be started with the Blended Learning Model. This study aims to analyse whether student learning outcomes are better with the application of Blended Learning using e-learning and to describe how student learning activities with the Blended Learning model. This type of research is a combination of quasy-experiment and descriptive research. The research data are the value of the midterm exam and the final semester test scores after the implementation of the learning Blended Learning model which will be analysed. Therefore, it can be compared students’ learning outcomes using the Blended Learning model with student learning outcomes with the conventional learning model. Based on the results of data analysis, it can be concluded that the N-gain test results obtained from the control class are better than the experimental class, which means that the use of the Blended Learning model is less effective in improving student learning outcomes in vector calculus subjects.
Study of Student Learning Habits and Their Relationship with Learning Outcomes in Elementary Linear Algebra Courses
Authors: Murni D , Helma H
Published: January 2021

The Elementary Linear Algebra (ELA) course is a compulsory and basic course for several other subjects. Most students have not mastered this material well. One of the contributing factors is the students' not good enough study habits. The purpose of this study was to analyze student study habits and their relationship with learning outcomes in the ELA course. This type of research is comparative causal research. The research instrument was a questionnaire and learning outcome test. The populations are students of the mathematics study program for the 2018 academic year. Students are divided into three groups based on their learning outcomes (x), namely groups: high (x ≥75), medium (60 ≤x <75), and low (x <60). The results showed that in general, student learning outcomes were at a good level, but study habits affected student learning outcomes only by 30%, meaning that there were other variables that influenced student learning outcomes. From the average study habits, it is known that students in the high group do better than the medium and low groups.
Analysis of Local Stability and Bifurcation in Cervical Cancer and Immune Cells Interaction
Authors: Winanda R S , Ahmad D
Published: January 2021

The dynamics of cervical cancer cells and immune cell interaction are modeled into a three-dimensional nonlinear differential equation system. It is a representation of the interaction model between one prey and two predators, with cervical cancer cells as prey and two types of immune cells as predators. The nature between these predators is a supportive relationship. Based on the mathematical model is obtained that there are six steady states in which three of them exist and the others do not exist for biological cases. Based on the analysis of stability near these steady states there has a bifurcation point about the steady states. By modifying some parameters value, there is a possibility to heal the cervical cancer sufferers.
The development of mathematics learning devices for junior high school students: a preliminary research
Authors: Santri F , Asmar A , Arnawa I M
Published: January 2021

Students’ problem solving skills (PSS) at SMPN 1 Teluk Kuantan and SMPN 3 Teluk Kuantan still low. This type of research is descriptive research with a qualitative approach. This preliminary research aims to know, review and explain: (1) Students’ PSS at grade viii of SMPN 1 Teluk Kuantan and SMPN 3 Teluk Kuantan (2) The Conditions of mathematics learning devices was used by teachers (3) whether to continue the development of research based on problem based learning to improve PPS. Based on observations from SMPN 1 Teluk Kuantan and SMPN 3 Teluk Kuantan, concluded that the learning devices used have not been able to facilitate students to learning mathematics. So it is necessary to develop learning tools based on problem based learning.
The development of mathematics learning devices based on problem-based Learning on equation system of three variables for tenth-grade senior high school students school
Authors: Arizon  , Irwan
Published: January 2021

This research aims to develop the learning device in the form of RPP (lesson plan) and LKPD (student's worksheets) based on problem-based Learning (PBL) for the material of Linear Equation System of Three Variables for tenth-grade senior high school student, which are valid, practical, and effective. The type of this research is development research. This research used a Plomp's model, which consists of three phases, namely preliminary research, prototyping phase, and assessment phase. This research has not been produced the learning devices that meet the criteria valid, practical, and effective. The result of the lesson plan and students' worksheets validation sheet showed that is developed was not valid. The learning devices are not also practical yet. While the effectiveness of the learning devices has not been seen in problem-solving and self-regulated Learning yet.
The development of mathematical learning devices based on model-eliciting activities and geogebra
Authors: Meisya S , Arnawa I M
Published: January 2021

This research aims to produce learning devices in the form of lesson plan (RPP) and students worksheet (LKPD) based on Model-Eliciting Activities and GeoGebra (MEA & GEO) to promote students’mathematical communication skills. The development model used is a Plomp model. The research data was collected through questionnaires, observation sheets, mathematical communication skills test, and interviews. The results showed that the learning devices based on MEA & GEO are valid, practical, and effective categories.
The Development of statistics learning design based on realistic mathematics education for grade VIII junior high school
Authors: Yuliasari N , Yerizon  , Arnawa I M
Published: January 2021

The results of the preliminary study indicated that there are problems in learning Statistics at grade VIII SMP/MTs. The students tend to be introduced to the use of formulas without involving the discovery of the concept itself and learning apart from the students’ daily experience. This underlies the design of the learning trajectory for Statistics topic and links them to activities based on students’ experience (experience-based activities). The purpose of this study was to design a learning trajectory of Statistics topic for grade VIII SMP/MTs based on the Realistic Mathematics Education (RME) approach, that equipped with a teachers’ and students’ books. This study was a design research that combines the Plomp model with the Gravemeijer & Cobb model. The subjects of this study were the students grade viii MTs Negeri Bangko. This study revealed a Statistics topic-learning design based on RME approach with the learning trajectory that were finding the mean concept, finding the mode concept, finding the median concept, and finding the five series statistical concept. The learning trajectories were implemented through the teachers’ book and students’ book. The results of conducting experiments showed that the context contained in a students’ book that can stimulate the students to develop their knowledge in finding statistical concepts. All strategies was found and discussed by students, show how the construction or contribution of students in finding the statistical concepts from informal to formal level. The results showed that the learning design of Statistics topic basd on RME approach was valid and practical. It was valid in terms of content and language and it was practical in terms of implementation, convenience and time required.
Problem-based learning in real number topic for practising critical and creative thinking
Authors: Adamura F
Published: January 2021

Research has been done is experiment research. Research has been done for knowing the effectiveness of problem-based learning in real number topic of Real Analysis 1. Applied learning devices have been developed in the last development research. This research is done in class VI B of Mathematics Education Program even semester of 2019/2020. Research outcomes show that implementation problem based learning in real number topic was effective. This is because lecturer capability in learning management was good, student activities were significant, student responses were positive, and classical student learning achievement was reached.
The Factors Encouraging Students to take Mathematics tutoring outside of School
Authors: Rada G , Fauzan A
Published: January 2021

The growth of tutoring is proliferating. Several studies showed that the development of the tutoring business could not be separated from the diminishing trust of informal public education in Indonesia. In addition, the program carried out by the tutoring trains to answer questions that might be tested in each test, both for national exams and college tests. This causes some students to try to take courses, so they can enter well-known schools and also to be able to continue their studies at favourite universities. The purpose of this study was to investigate the factors that encourage students to take tutoring outside of school. This type of research was qualitative research. The data collection technique used was snowball by using an interview guide as the research instrument. The research subjects consisted of a student who participated in the tutoring as the key informant and other informants, namely her parent, her school mathematics teacher, her classmates, and other students who took tutoring in the same place. The collected data were analyzed descriptively and qualitatively. The technique of ensuring data validity was triangulation. The results revealed that four factors encouraged students to take mathematics tutoring outside of school, namely: (1) Parent' desire, (2) Student' desire to be an excellent student like her classmates, (3) Difficulties in understanding the material explained in the classroom, and (4) Realizing ideals.
The Development of Learning Device Based on the Discovery Learning Model Which is Oriented with Natural Disaster Mitigation Towards Mathematical Analyzing Ability of Pupils in Grade VIII of Middle School
Authors: Tusa’diah R , Yerizon  , Asmar A
Published: January 2021

This study aims to produce mathematics learning media based on the discovery learning model, which is oriented to natural disaster mitigation towards the mathematical analyzing abilities of grade VIII in Junior High School students. This media was developed based on the results of a mathematical analyzing skills test and the results of a questionnaire on disaster mitigation at MTsN 1 Padang. Based on the results, it can be concluded that the mathematical analyzing abilities of students were still very low by the achievements of 20.7%. However, in natural disaster mitigation, the survey results showed that 88.9% of MTsN 1 Padang students showed that there was no socialization and learning treatment about natural disaster mitigation. This research is development research. The development model used in this research was the Plomp model, which consists of three phases: preliminary research, prototyping phase, and assessment phase. The results of the analysis of the data based on the lesson plan (RPP) and (LKPD) validation sheets showed that the mathematics Learning media based on the discovery learning model which oriented to natural disaster mitigation on the mathematical analyzing abilities of students have been valid in terms of construct and content and have also been practical in terms of implementation and easiness of use. which an average of 80.3% and it was also effective for improving students' mathematical analyzing abilities by achievement of 73.84%.
Problem based learning and mathematical problems solving skills of junior high school students: a preliminary research
Authors: Isriani W P , Musdi E , Arnawa I M , Asmar A
Published: January 2021

This study is research and development (R & D). A preliminary research of this R & D aims to get information about students' Mathematical Problem Solving Skills (MPSS) and learning device (known as RPP and LKPD) that the teacher used in learning. Initial observations at SMPN 1 Pariaman and SMPN 2 Pariaman show that: (1) students' MPSS is still low, (2) only a small portion of students are actively involved in the learning process and only some of the students take the learning seriously, (3) The LKPD that the teacher used in learning can’t help students in learning mathematics. Based on this, follow-up is needed in the form of R & D to develop mathematics learning devices based on problem-based learning for junior high school students.
The validity of inquiry-based mathematics learning tools using the Malay context of Rengat to improve mathematical reasoning skills
Authors: Sefni P , Yerizon  , Asmar A
Published: January 2021

Students' low ability in mathematical reasoning is influenced by the learning model applied in the classroom. Such as the use of learning tools with a context that is closer to students' life. The purpose of this research is to produce inquiry-based learning tools with a Malay Rengat context that is valid in improving students' mathematical reasoning abilities. This development research uses the Plomp model which consists of three phases, namely preliminary research, prototyping, and the assessment phase. The validation of the tools was carried out by three mathematics education experts, one educational technology expert, and one Indonesian language expert. The average scores obtained from the validation are 3.57 for the validity of the lesson plans and 3.55 for the validity of the worksheet. With these results it can be concluded that the development of inquiry-based mathematics learning tools with the Malay Rengat context is valid.
The Application of Cloud Computing in College English Teaching
Authors: Liu Yang
Published: January 2021

In the era of Internet linking everything, education faces all-round multi-dimensional challenges and opportunities. In order to utilize the advantages of technology, college English educators should extend the traditional classroom aided by big data and Cloud computing in education. Cloud computing is emerging as a powerful computing paradigm with its aim of efficient resource utilization, providing a new solution for online English teaching and learning. With specific teaching targets of college English, the pre-class, in-class and after-class teaching phases aided by Cloud computing are presented, which aims to provide inspiration for educators, administrators and learners.
Research on SLAM of Large-scale Space Point Cloud Topological Robot
Authors: Wu Changzhong , Ge Fan , Shang Guangchao , Wu Liang , Guo Hengshuai , Zhao Mingpeng , Wang Guitao
Published: January 2021

This paper focuses on the cutting-edge and difficult issues in the field of navigation and positioning of service robots. By exploring the technical approach to practical service robot environment modeling and positioning, a new topology SLAM method is proposed. This paper focus on the key technologies involved in 3D SLAM of robots based on large-scale spatial point clouds, including back-end optimization and closed-loop, high-precision sensor calibration technology. Further propose topological map construction strategies, and focus on designing map-generating algorithms based on graphs or tree structures. The research results will provide innovative ideas and practical solutions for environmental modeling and navigation control of service robots, and strongly support technological advances in related research fields based on three-dimensional environmental perception.
Faster Finding of Optimal Path in Robotics Playground Using Q-Learning with “Exploitation-Exploration Trade-Off”
Authors: Yuan Xiaosong
Published: January 2021

It has been a challengeable task to use algorithms designed for specific situations to plan path in real-world environments, because of the uncertainty and unpredictability of the real world. However, learning algorithms have an effective adaptability to deal with real-world problems such as path planning. Because rules of how to work specifically are not preestablished but to be learnt. In this research, q-learning is modified with “Exploitation-Exploration Trade-Off” to find an optimal path faster in Robotics Playground, which is a real-world simulation in Simulink. “Exploitation-Exploration Trade-Off” is a strategy that enables q-learning to explore more parts of the environment so that it is able to learn more information within the same number of episodes. Four kinds of exploration probability are analyzed, which are linear (to episode), quadratic, hyperbolic and no exploration. The results show that the quadratic one has the best behavior among the four kinds of exploration probability when α and γ (two parameters in Bellman equation used to update q-table) are both 0.9. When α and γ are 0.9, the quadratic one is 27.41%, 32.53% and 36.66% faster than linear, hyperbolic and no exploration one with respect to the number of episodes used to find an optimal path. Effectiveness of balancing exploitation and exploration in q-learning is already proved in previous works, but the novelty of this research lies in testing specific exploration probabilities with different parameters (α and γ) and finding the most effective one as well as combining q-learning with real-world simulations. This research can help other q-learning-related works to save time and get a better result.
Video stabilization algorithm for tunnel robots based on improved Kalman filter
Authors: Liu He , Cui Shiyi
Published: January 2021

The application of tunnel robots in power tunnels is becoming more and more extensive. Due to various reasons, video jitter will inevitably occur during the inspection process, which will affect the real-time processing of subsequent images. In order to reduce the impact of video jitter and meet the requirements of real-time image processing, it is necessary to study fast video image stabilization methods. The traditional Kalman filtering algorithm has less computational cost, but it will cause a large estimation error when the system motion state changes. In this regard, the paper proposes an improved Kalman filtering algorithm, which changes the corresponding filter parameters by real-time estimation of a system in motion to reduce the estimation error. And this algorithm performs Harris corner extraction in the set feature dense area, and combines the PyrLK optical flow algorithm with the homography matrix to calculate the motion trajectory, which improves the calculation speed and accuracy of the algorithm. Experimental results show that this algorithm has better video stabilization effect and faster calculation speed compared with the traditional Kalman filtering algorithm, and can better meet the real-time image processing requirements in the inspection process.
Weakly supervised object detection based on deep metric learning
Authors: Dong Weiming , Zhang Xiaoming , Zhao Ping
Published: January 2021

Weakly supervised object detection is a hot issue in the computer vision field, which aims to train a high performance detection model with low cost annotation data. The existing methods of weakly supervised object detection only summarize the object category but don’t consider the similarity between the objects in the optimization process. For solving this problem to improve detection accuracy, this paper proposes a weakly supervised object detection model based on deep metric learning. In the initial training phase, an initial metric has been learned in advance to measure the similarity between these objects; in the correction phase, we propose an adjacent instance mining method based on proxy samples, this approach expands the model’s recognition view, and prevents premature locking the wrong object position. We design a series of experiments on the PASCAL VOC2007 dataset to prove the effectiveness of this method.
Modelling and Calculation of Seismic Wave Field Caused by Moving Ship
Authors: Peng Fei , Lv Junjun , Wang Xu
Published: January 2021

In order to study the frequency characteristics of seafloor seismic waves generated by sailing ships. The finite element numerical simulation method is adopted and COMSOL Multiphysics software is used to establish a two-dimensional axisymmetric model to calculate the distribution of seismic wave fields and compare the correlation between horizontal and vertical components at different depths when the depth of seawater is same. The results show that the frequency characteristics of the horizontal and vertical components become larger and larger as the sound source approaches the seabed, and the influence of seawater on the vertical direction is more and more significant. According to the research results, the seismic wave characteristics of different components can be used to judge the specific information of the sailing ship more accurately.
Study on the Performance of Light and Shadow Effect in Computer Aided Interior Design
Authors: Li Siyuan , Huang Jian
Published: January 2021

With the rapid development of computer technology in China, interior design has also ushered in a broader space for development. Light and shadow effect is an important trend in art design. Through the designer’s artistic accomplishment, we can further enhance the presentation ability of interior design lighting effect. In practical design, we need to fully consider the physical properties of real life materials. Through reasonable rendering, we can achieve the best performance of light and shadow art. This paper first analyses the significance of Computer Aided Interior design. Then, this paper analyses the light and Shadow form and artistic expression of computer-aided interior design. Finally, this paper designs the interior lighting design effect of a hotel.
Sentiment analysis of Chinese text based on Elmo-RNN model
Authors: Yang Min , Xu Junwu , Luo Kai , Zhang Yiping
Published: January 2021

Traditional word embedding technologies such as Word2Vec and GloVe can only produce a single semantic vector, and can not get the specific meaning of polysemous words in the text combined with context. To solve this problem, a sentiment analysis model of Chinese text based on Elmo and recurrent neural network is proposed. The model uses Elmo model to learn the pre training corpus, and the BiLSTM network structure in Elmo model makes the word vector generated by Elmo model have context sensitive characteristics, which is more accurate in the expression of polysemous words, and this is not achieved by the traditional word embedding technology. Then, the model uses the recurrent neural network to extract the deep-seated features of the word vector and fuse the features. Finally, the softmax function is used to realize the sentiment classification of text. Experimental results show that Elmo-RNN can effectively improve the accuracy of text sentiment analysis.
An Robot Vision Grasping Network Based on Inception-Lite
Authors: Xu Liang , Zhou Ziwei , Wang Chaoyang
Published: January 2021

Using computer vision algorithms to find the grasping position of unknown objects is a key issue in the development of intelligent robot technology. In order to address the problem of robot grasping, firstly, based on the analysis of Inception-v1, a novel Inception-Lite convolution module is proposed, and then a grasping network GRASPNET-CLF is designed based on the convolution module. Finally, the grasping network GRASPNET-CLF is trained and verified by GPU on Cornell grasping dataset. The experimental results show that the GRASPNET-CLF has better performance in the same kind of network, and meets the real-time requirements.
Identification of Maize Leaf Diseases based on Convolutional Neural Network
Authors: Wu Yuhao
Published: January 2021

The identification and diagnosis of crop leaf disease is of great significance to improve the quality of crop cultivation. Compared with the traditional manual diagnosis method, the automatic identification of crop leaf disease based on computer vision technology has high efficiency and no subjective judgment error. But the traditional image processing technology is affected by different illumination conditions, cross shading. The algorithm’s robustness is affected. Because deep learning dose not need to set learning features manually, which greatly improves the recognition efficiency. In this paper, the two-channel Convolutional Neural Network was constructed based on VGG and ResNet. Taking the maize leaf diseases as research objects, the maize leaf disease data set has been constructed and preprocessed. And the structure and characteristics of AlexNet, VGG and ResNet are introduced respectively. By adjusting the parameters of the two-channel Convolutional Neural Network, the accuracy of identifying the maize leaf disease type in the validation set can reach 98.33%, while the VGG model can reach 93.33%. The classification results on three types of maize leaf diseases show that the two-channel Convolutional Neural Network has a better performance than the single AlexNet model.

Three kinds of leaf disease data sets (big spot, gray leaf spot and rust ) are downloaded from the “kaggle”platform.
Study on Data Management of Electric Power Distribution Dispatch and Cutting-through Based on Big Data
Authors: Cai Yingkai , Cao Shilong , Xu Jingjing , Mu Rong
Published: January 2021

With the development of technologies such as big data, Internet of Things, and cloud computing, power companies’ information management systems for dispatch, finance, projects, marketing, equipment, and materials have also made great progress. The construction of the data platform of the electric power company has realized the unified collection of data resources for the entire business of the power grid, which provides strong support for cross-professional, cross-departmental, internal and external data association analysis and in-depth data value mining. Based on the data management and management of power distribution dispatching, big data technology is adopted to optimize the coding and structure of power system distribution data to realize data cutting-through. Through experimental testing and analysis, it is proved that the research content of the paper can meet the current power operation needs. Compared with other data management methods, it can achieve higher management accuracy and coverage, and play a data guarantee effect for power distribution work.
Optimization of cutting parameters of microwave expanded stem in slim cigarette
Authors: Pan Wen , Feng Hongtao , Chen Ran , Li Xin , Hu Qun , Yang Lei , Wu Jiacan , Zou Juan , He Xufeng , Pu Qian , Zhao Meiwei , Qiu Ye , Zhang Tao
Published: January 2021

In order to explore the cutting optimization parameters of the processing technology of the expanded stem of slim cigarette, according to the quality characteristics of the microwave expanded stem, the samples with different combinations of cutting parameters were optimized. The results showed that under the combination of cutting parameters with a slicing thickness of 0.5mm and cutting width of 0.2mm, the high whole cut tobacco ratio, moderate small particle content, and minimum tobacco loss from cigarette end could be obtained. At the same time, the samples also had a lower hazard index and better sensory quality. This study provides a reference for technical research on the processing technology of the expanded stem of slim cigarettes.
Integrated scheduling algorithm for multiple complex products with due date constraints
Authors: Wang Xinkun , Song Yuchuan , Zou Yuji , Guo Weifei , Wang Yi
Published: January 2021

A new scheduling algorithm based on improved genetic algorithm is presented to solve integrated scheduling problems in small batches of multiple complex products with different manufacturing and due dates. This algorithm virtualizes the schedule of multiple complex products into a single scheduling of one product by establishing a relationship matrix of operation of multiple products to facilitate complete optimization. In addition, novel coding, crossover, mutation, and encoding are designed for the improved genetic algorithm. The proposed integrated scheduling algorithm is tested by existing examples. Experimental results validate the effectiveness and superiority of the proposed integrated scheduling algorithm.