bluuebunny/crossref_metadata_2025 huggingface dataset:
A note of a predator-prey model with additive allee effect and feedback controls
Authors: Ren Huailan
Published: March 2020

This study focuses on the investigation of a predator-prey model with additive allee effect and feedback controls. First, we get sufficient conditions of system persistence. Second, the conditions for the existence of the systems global attractivity areestablished by constructing suitable Lyapunov functions. Additionally, by applying the fixed point theory and some new analysis and calculation methods, we obtain the existence and uniqueness of the positive periodic solution of the system. Eventually, we validate the theoretical results by numerical simulations.
Research on Technology of Finger Vein Pattern Recognition Based on FPGA
Authors: Yong Yan
Published: March 2020

Finger vein patterns recognition as a new type of biometric recognition technology, the existing pattern of the finger vein recognition has registered and verified the disadvantage of slow speed. Therefore, in this paper, a finger vein feature extraction and matching recognition algorithm suitable for FPGA is proposed, and the hardware system of finger vein recognition is designed and implemented, including three modules: finger vein image acquisition device, image processing module and matching display module. Finally, the key algorithm is simulated and analyzed. It speeds up the registration and recognition of mobile phone veins.
Evaluation and Comparison of Deformable Image Registration Algorithms for 4D CT Images
Authors: Hu Xiaokun , Shao Guangpu , Yang Jimin , Yang Juan
Published: March 2020

Deformable image registration (DIR) is crucial in adaptive radiation therapy. However, the validation is a challenging work due to the lack of gold-standard. This study proposed an evaluation framework by using point-to-point displacement vector field (DVF). Three DIR algorithms including original flow, active Demons and symmetric force Demons were validated for ten lung 4D CT images with landmarks. DVFs derived from DIR algorithms (dDVF) and manually measured according to landmarks (mDVF) were analyzed and compared. Their target registration errors (TRE) and the relationship of lung motion and three-dimensional TRE were explored. The distance discordance metric (DDM) values were calculated. For all cases, the active Demons algorithm had the smallest TRE value and DDM values and it showed the poor correlation between 3D TRE and lung motion, which indicated that this algorithm outperformed other DIR algorithms enrolled in this study. Preliminary results demonstrated that the proposed evaluation framework had a potential ability of providing clinical guidance for the selection of appropriate algorithm in radiation therapy.
Adaptive tracking algorithm based on target moving speed
Authors: Liu K W , Wu D W , Xie Q S , An Z Y
Published: March 2020

To solve the problem of tracking failure caused by fast object movement, we proposed an adaptive tracking algorithm based on object movement speed on the basis of ECO-HC (ECO for hand-crafted feature version). First, correlation filtering method was used to obtain the distance of the maximum response point of adjacent frames. Then the speed of the object was judged by the distance of the maximum response point of the adjacent frame. Then the number of scale filter factors and interval frames updated by the template were adjusted on the basis of the object’s speed. The comparative experiment on the VOT2016 benchmarks showed that our tracker improved the accuracy and robustness of object tracking.
Innovation and Practice of Teaching Reform in Higher Vocational Colleges under Artificial Intelligence Environment
Authors: Cheng Yunli , Bu Xu
Published: March 2020

With the great success of machine learning algorithms represented by deep learning in the fields of machine vision and speech recognition, as well as the abundant and continuous data resources provided by cloud computing and big data, artificial intelligence has entered an unprecedented period of rapid development. Deeply changing all walks of life, the state requires corresponding intelligent education, but the artificial intelligence + education road is how to go, there is still no systematic discussion. In view of this article, artificial intelligence is used to promote the research of teaching reform, in order to enlighten the application of artificial intelligence teaching and promote education. The intelligent development of teaching.
Redis rehash optimization based on machine learning
Authors: Zhang Juan , Jia Yunwei
Published: March 2020

This paper proposed an optimization for Redis rehash based on machine learning. Since Redis involves numbers of dimensions, only the process of Redis rehash was studied. In order to use Redis correctly, the principle of Redis rehash was analyzed firstly and then pointed out a feasible optimization scheme. Aiming at solving the problems that might exist in the rehash process, the scheme could predict the number of keys based on the time series model ARIMA. The result showed that the scheme was feasible in practice.
Cattle Face Recognition Method Based on Parameter Transfer and Deep Learning
Authors: Wang Hongyu , Qin Junping , Hou Qinqin , Gong Shaofei
Published: March 2020

In order to accurately identify cattle, help insurance companies to deremine claim of Cattle Insurance, or realize inteligent breeding. We need a cattle face recognition technology. Due to the difficulty in data collection of cattle face and the small amount of data, it is difficult to apply deep learning method to cattle face recognition. So there is a method combined with tarnsfer learning effectively trains the model,or initializes the network weights by parametric transfer. This paper proposed using VGGFace dataset pre-training network to solve the problem of small sample face recognition. The VGG-16 deep convolutional neural network model was used to extract the cattle’s face features. The model parameters obtained by training the VGGFace face dataset were used to initialize the network weights, and then the network was trained on the cattle face dataset. The experimental results show that the method can obtain 93% recognition accuracy under small sample data.
Optimization analysis of autonomous obstacle avoidance path for self-driving vehicles based on improved ant colony algorithm
Authors: Li Shixuan
Published: March 2020

In order to further improve the accurate response and fast response ability of autonomous obstacle avoidance in real dynamic environment, an improved AC (ant colony algorithm) model based on PSO (Particle Swarm Optimization) is proposed to realize the global and fast optimization analysis of autonomous obstacle avoidance path planning for self-driving vehicles. Firstly, an autonomous obstacle avoidance path planning model for self-driving vehicles is established; secondly, the global pheromone is searched by using the cooperation and information sharing mode between particles in PSO; thirdly, the global pheromone updating strategy is used to optimize the path searching ability of AC; finally, an innovative way of integrating PSO and AC is used to obtain the optimal path of autonomous obstacle avoidance for self-driving vehicles. The simulation results show that the AC model is improved by PSO to realize the fast and accurate planning of autonomous obstacle avoidance path for self-driving vehicles.
Design and Research of Control System of the UAV Photodetector Stabilized Platform
Authors: Zhao Xinglong , Liang Shaojun , Liu Fulin , Ding Fangzheng , Zhao Jingshan , Zhuo Jiajing , Xu Luoting , YunLiu
Published: March 2020

Based on use conditions and functional requirements of the UAV photodetector stabilized platform, an overall design scheme of its control system is given. This system is studied from hardware and software. Development of the platform is prospected.
Topology optimization method and lightweight design of anthropomorphic manipulator
Authors: Wu Haoting , Yin Meng , Zhao Zhiliang , Xu Zhigang
Published: March 2020

The topology optimization method is adopted to realize the weight reduction of the mechanical arm under the rigidity condition. The typical configuration and load conditions of the manipulator are analyzed. The stiffness mass model of the part is established by the finite element analysis method, and the topology optimization of the part is carried out. On this basis, the optimization of the manipulator system is completed. The optimized art quality is reduced by 6.2% compared with the original model, and the stiffness meets the standard requirements. The method of combining virtual joint method with working space of the manipulator is proposed. The amount of end deformation after optimization of the manipulator is analyzed. The maximum deformation is 0.27mm, which meets the requirements. The optimization process provides a reference for the selection of the drive joint parts of the mechanical arm. According to this, the physical design of the mechanical arm is completed and the load test is carried out. The experiment shows that the mechanical arm can still achieve rapid response under the condition of a 2kg end load.
A New Method of Gene Information Vectorization and its Application in Similarity Search
Authors: Dong Qian , Zhang Zhaogong , Zheng Dayuan , Zhang Zhiliang
Published: March 2020

With the development of the Human Genome Project, more and more biological sequence data are generated, and the analysis and processing of these sequence data have promoted the development of bioinformatics. Sequence similarity analysis is the basis of bioinformatics, through which we can use the information of known sequences to study the structure, function and evolutionary relationship of unknown new sequences. This paper performs data compression and retrieval on the genome database based on the dbSNP information of DNA. According to the rule of determining a protein by three bases, the amino acid characters are determined, and the redundant information is removed by using the dbSNP information. It is the first time to propose the construction of a new compressed form of biological sequence structure, which can reflect the strong correlation between the SNP location information and SNP in each sample in the genome. Finally, this paper constructs a complete biological sequence approximate neighbor query system, which can not only greatly reduce the storage and computing overhead, but also improve the query efficiency under the condition of ensuring the retrieval accuracy. The accuracy and scalability of this method are verified by experiments on a large data set of gene database.
Simulation Research on Optimal Installation Position of partially-filled pipe Electromagnetic Flowmeter sensor
Authors: Jiang Yulin , Jiang Wuzhi , Zhang Chao
Published: March 2020

The measurement sensor of the partially-filled pipe electromagnetic flowmeter must be installed where the fluid is fully developed. For partially-filled pipe electromagnetic flowmeters, the boundary conditions vary with the fullness of the pipe, and the distance from the pipe inlet to the fully developed is not the same as the distance of the full-filled pipe flowmeter. Therefore, in the partially-filled pipe state, the mounting position of the electrode sensor is also different from the mounting position at full-filled pipe state. Based on the measurement principle of electromagnetic flowmeter, the finite element simulation method is used to simulate the flow field development of partially-filled pipe electromagnetic flowmeter. First, according to the knowledge of hydraulics, the variable boundary constraint is established, and then the simulation model of flowmeter fluid development process is established. Second, the Comsol Multiphysics finite element simulation software is adopted to solve the fluid development process, and the required distance for the full development of the partially-filled pipe fluid is obtained. Finally, the optimal installation position of the partially-filled pipe electromagnetic flowmeter sensor was determined based on the distance. The simulation results prove the feasibility of the proposed method, which can provide powerful theoretical support for the fabrication of partially-filled pipe electromagnetic flowmeters.
Warships Formation Identification Based on Neural Network
Authors: Tian Liye , Wu Jinping , Wang Yongjie
Published: March 2020

Submarine tactical software should be able to identify warships formation. Neural network based pattern recognition method is introduced in submarine tactical software to improve accuracy. This paper discussed how to transform sensor data into training samples by using Matlab, and how to choose training samples. Experiments indicate that GRNN network is capable of this work and suitable for actualizing.
Application of Artificial Bee Colony Algorithms in Smart Grid
Authors: jie YU
Published: March 2020

The operation of modern society cannot be separated from the power network. The power output from power stations is transmitted to all walks of life through the power network. The same people’s activities are inseparable from electricity. In recent years, the scale of the power grid has become larger and larger, and communication technology and control technology have also developed rapidly. The emergence of smart grid is to meet the needs of users, optimize resources, balance supply and demand relations, etc. The development of artificial bee colony algorithm also provides a new dispatching method for smart grid. This paper studies the application of artificial bee colony algorithm in fault location of distribution network, so as to make the smart grid develop better.
SoC Implementation of Visual-inertial Odometry for Low-cost Ground Robots
Authors: Liu Bo , Li Lin , Liu Hengzhu
Published: March 2020

Simultaneous localization and mapping (SLAM) is a key technique for autonomous navigation of a robot in unknown environments. Recently, many researchers focus on the algorithm optimization of visual-inertial odometry (VIO) as a real-time SLAM method. In this paper, different from pure algorithm research, we propose a novel low-cost hardware solution based on the system on a chip (SoC) to achieve real-time performance and low power consumption of VIO. We design a novel hardware architecture to accelerate a popular VIO algorithm, with the time-costly image feature processing computed on FPGA and floating-point numerical computation been done on ARM. On the algorithm side, we fuse odometry information from the two wheels of the ground robot in a unified optimization method, which results in accurate metric scale estimation of the VIO. The experimental results show that our design and implementation have superior performance for ground robot applications, allowing real-time localization of a robot with a very low-cost hardware for computation in complex environments.
Distributed System Based on Publish-Subscribe Middleware Automated Testing Method
Authors: Wang Xingmin , Yang Meng , Han Peng
Published: March 2020

Publish-subscribe middleware has been widely used in military, medical and other important industries because of its good characteristics of time and space decoupling. With the continuous expansion of the scale of distributed system application, the problems in distributed system testing, such as low efficiency and difficulty in fault positioning, are more prominent. In this paper, an automatic testing method is proposed, which is based on the characteristics of the publish-subscribe middleware systems. Based on an independent designed and implemented publish-subscribe system named BlueDCS, a corresponding automatic testing tool is developed. Application verification shows that the tool can well solve the aforementioned problems in distributed system testing.
Automatic Target Recognition for CT-based Airport Screening System
Authors: Gu Chen , Dong Zhen
Published: March 2020

To construct an automatic target recognition CT-based screening system for airport, this paper introduces a method by combining two hot technology, CT Image Processing and Machine Learning together. With the grey-scale features and the Histogram of Oriented Gradient (HOG) features extracted from the CT images, we can train different classifier (SVM, KNN) to recognize the targets (saline, rubber, clay) we want. By comparing the recall rate and precision rate of each classifier, we may find the best classifier for this project.
Wireless Sensor Network Node Deployment Based On Regular Tetrahedron
Authors: Zhang Yunfei , Zhang Zhaogong
Published: March 2020

The deployment of nodes in 3D wireless sensor networks has always been the focus of research. In this paper, two algorithms for achieving full coverage are proposed, which are applicable to the even distribution and the uneven distribution of target points. Among them, the characteristics of the spherical and regular tetrahedrons are rationally utilized to achieve the purpose of covering more space with the same nodes. For the three-dimensional space with uneven target distribution, it is proposed to use the DBSCAN clustering method for target point processing, and then perform the same processing in each cluster to avoid wasting more sensor nodes and increasing energy consumption. Finally, considering the problem of energy saving, some nodes are put to sleep and waiting for the next call to reduce energy consumption and extend the working time of the network. Simulation experiments show that the algorithm has improved the number of nodes used and energy consumption.
Non-contact sensing and modelling on the voltage and electric field for powered transmission line
Authors: Fuqing Yuan , Lu Jinmei
Published: March 2020

From environmental, ecological or technical perspectives, the measurement of the potential voltage, electric field near the power transmission line is essential to ensure the exposure level does not exceed the limits of the government specified. This paper develops a potential voltage model, designs experiments to verify the developed model and to evaluate the parameters in the model. An electric field model is derived from the voltage model further, which can be used to estimate the electric field strength numerically. The models are found consistent with the observed data in the experiment.
Electric Tower Target Identification Based on High-resolution SAR Image and Deep Learning
Authors: Tian Guishen , Meng Song , Bai Xuejiao , Liu lijuan , Zhi Yang , Ou Wenhao , Fei Xiangze , Tan Yuanpeng
Published: March 2020

With regard to the electric tower identification problem in the high-resolution SAR image under complicated background, it is difficult for the existing target detection algorithm to reach balance in identification precision and efficiency. Therefore, this paper combines the advantages of high YOLOv2 calculation efficiency and high VGG classification precision, and proposes a kind of two-stage target detection algorithm of YOLOv2 and VGG cascade connection by virtue of transfer learning. At the Stage-1, the sliding window and the non-maximum suppression algorithms are combined, and the YOLOv2 is utilized to conduct rapid electric power detection to the whole SAR image, thus obtaining the target detection result of high recall rate; at the Stage-2, the VGG classification model is utilized to conduct secondary classification to the target and background with regard to the target detection result at the Stag-1, thus further eliminating the false positive. This algorithm can be used to enhance the accuracy of only using YOLO v2 f or target identification, thus reducing the false alarm rate of model effectively. The electric towers in the mountainous belt and the plain belt are used as samples for training, thus enhancing the robustness of algorithm. After conducting algorithm testing to the COSMO image in Zhijiang City, Hubei Province, the result shows that the electric tower recall rate can reach 73.8%. The method adopted in this paper can identify the electric tower target of the whole SAR image more accurately through model transfer and two-stage deep learning, thus being further promoted into actual application scene.
Overview Of Serverless Architecture Research
Authors: Jiang Lizheng , Pei Yunman , Zhao Jiantao
Published: March 2020

With the development of cloud computing, serverless computing, that is, function as a service (FaaS) is considered to be the next stage of cloud computing development. Serverless computing can be seen as a logical extension of cloud computing, a disruptive approach to application development. It is based on the code written by the developer for accurate resource allocation, and the resources on the platform are started when a predefined event is triggered. By comparing with the traditional architecture, the advantages of serverless architecture are highlighted. Although serverless is a relatively new concept in the field of software architecture, it is also a technological innovation with great influence.
Spectral efficiency analysis of millimeter-wave heterogeneous cellular networks based on PCP
Authors: Lu Yi , Jia Xiangdong , Chen Yuwan
Published: March 2020

To solve the interference problem in densely populated areas, this paper proposes a new scheme that taking user equipment (UE) as the center of millimeter-wave heterogeneous networks based on Poisson cluster process (PCP) model. Under the network model, the UE is divided into cluster-center UE or cluster-edge UE by using the ratio of the first and second closest distances of the UE from pico base stations. The random geometry and PCP methods are used to study the downlink spectral efficiency and the correct derivation results are verified. At last, the influence of relevant parameters about system performance is analyzed.
Research on precise organization of operational network based on “Chains in Network” model
Authors: Li Fangsheng , Hou Jianfeng , Qu Lianzheng , Fan Binbin
Published: March 2020

Aiming at the network organization requirements of precise operation, this paper proposes a precise network organization method based on the “network in the chain” model: starting from the command perspective, setting the intelligence chain, C2 chain, strike chain and service chain as the entry points, the organizing and planning of operation network is classified and carried out. The model of “chain in net” is formally described, and the evaluation index system of “chain” to “net” is established. This kind of precise network organization based on command perspective is of great significance to improve the ability of joint operation based on network information system.
Research on the Formulation of the Motion Angle of Human Lower Limb Joints
Authors: Wang Kaiyuan , Dai Zongmiao
Published: March 2020

In order to make the exoskeleton recognize and predict human gait after walking for a period of time, the control system of the exoskeleton must establish the human gait database in real time. The traditional method is to collect the discrete point data of the angle of each lower limb joint changing over time and then store it in the database. There are two main disadvantages: first, the data volume is too big to store everyone’s gait data which is unrealistic; second, it is slow to find the optimal matching of gait process one by one from the database, and it will affect the speed of the gait recognition and prediction, so the exoskeleton is slow to respond. Therefore, it is necessary to find a method dividing human gait data and fitting the function of each piece of data, namely, the formulation of joint motion angle. This paper presents a research on the formulation of the motion angle of human lower limb joints.
Mosaic Method for Insulator Images Based on Region Characteristics
Authors: Wang Zhihui , Zhang Licai , Yao Chenhong , Sun Xiaoyan
Published: March 2020

Based on the analysis of image registration methods based on region and feature, an image mosaic method based on edge statistical features is proposed. By using the mean and mean square deviation of RGB parameters of edge region pixels as matching criteria, this method improves the shortcomings of general region registration algorithm, such as large computational complexity, sensitivity to brightness and scale changes, poor real-time performance and adaptability, and simplifies the calculation of global region features of images to that of local region features. This method makes full use of the relevant information of the RGB parameters of the pixels. Compared with the registration calculation based on gray level, it has the characteristics of abundant optional features and high practicability.
Clustering with Fuzzy C-means and Common Challenges
Authors: Deng Songyin
Published: March 2020

Clustering algorithm is massively used in various fields such as computer vision and so do FCM. One of the purpose of this paper is to generate new insight into improvement on general clustering algorithms through this inspection of one specific clustering algorithm (FCM) help. Three common challenges of clustering (Noise problem, long operational time, and initial bias) in Fuzzy C-Means algorithm and corresponding solutions to each of these problems are introduced. Further potential of meta learning and adversarial learning to solve the clustering problems are recognized in the end.
A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation
Authors: Chen Yuxi , Zhang Xiaotong , Zhao Qing , Akhtar Faheem
Published: March 2020

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user’s behaviour, which means a user’s interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn’t consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.
A Study of Country Economic Risks in Countries Along the Belt and Road: A Classification Method Based on DEA Sort
Authors: Zhou Yujie
Published: March 2020

Today, with the development of international trade and prosperity, the implementation of the Belt and Road strategy has brought a large number of international trades, international lending and cross-border investment business. In the process of business decision-making, the investigation of national economic risks is indispensable. To solve this problem, this paper adopts the idea of DEA Sort, selects relevant economic indicators, combines the input-oriented CCR multiplier model and cluster analysis to classify the national economic risks of sample countries, and finally obtains three classifications and the economic risks of most countries were found to be high. It is expected to provide reference for relevant cross-border business in the countries along the Belt and Road and relevant enterprises.
Research on Evaluation Model of College Students’ Mathematical Modeling Ability Based on Hierarchical Clustering Method
Authors: Qi Yuanhua
Published: March 2020

In order to make the evaluation of mathematical modeling ability more in line with the actual ability of college students, it is necessary to establish a scientific and objective evaluation method. Therefore, based on the hierarchical clustering method, this paper establishes the evaluation criteria of College Students’ mathematical modeling ability. Through the test, the application of this method can make a fair judgment on the mathematical modeling ability of college students, make the evaluation of the mathematical modeling ability of students more based by educational institutions, and also help students to provide useful guidance for personal learning progress. In addition, this method can guide teachers and researchers to carry out the work of mathematical modeling, and help them to cultivate more mathematical modeling talents.
A Self-Control Analysis Method of University Students Based on Campus Big Data
Authors: Ge Liang , Yu Jia , Zhou Qing , She Yanyun , Yuan Zhongwei
Published: March 2020

Nowadays, the analysis of university students is the main concern of educational institutions. In this paper, we investigate the problem of analysing students’ self-control ability. Our model focuses on the process of feature extraction, and then classification algorithms are applied. We propose the concept of behaviour chain to model the behavior of student. On this basis, we propose a spatial periodic feature extraction method based on the longest common sub-sequence(LCS). And we extract some statistics features from consumption data, access control data and others. Our model SCA(Self-Control Analysis) performs with the precision of 0.72, the recall of 0.78 and the Fl-score of 0.74. It means that it’s possible to analyse student’ self-control ability with campus big data.
The secrets of mindful STEM teaching and learning
Authors: White A L
Published: March 2020

The pressure upon a Science, Technology, Engineering or Mathematics (STEM) educators has increased as the demand for workers with STEM capabilities develops while worldwide the number of students electing to specialise in STEM areas is declining. In 2018, the Indonesian Ministry of Industry (KPRI) released a document titled ‘Making Indonesia 4.0’, stating STEM teachers will be expected to produce students who have several “careers” during their working lives and have the skills needed which includes technical capabilities, creativity and innovative problem-solving. This will occur in a context where the future of work remains human and so while the new technologies have the capacity to automate many tasks, they also create as many jobs as they replace. As well as these governmental expectations, STEM educators are being continually confronted with an avalanche of advice on the best ways of teaching and learning their subject. This advice comes from across the spectrum of expertise that includes parents, outstanding teacher heroes, commercial interests, and dubious ‘experts’ who have no pedagogical knowledge or experience. This paper will attempt to cut through this avalanche and dig down to the bedrock of successful and sustainable STEM teaching and learning. While this paper is written for all STEM educators, it will tend to favour examples from the mathematics and brain research disciplines.
Students’ mathematical communication in discovering the concept of graphs of trigonometric functions through instructional videos
Authors: Detiana  , Johar R , Mailizar
Published: March 2020

Mathematical communication is one of the critical aspects of learning mathematics. One way to improve mathematical communication skills is integrating technology in learning. The technology that can be employed is an instructional video, for instance, GeoGebra tutorial video. The video helps students discover the characteristics of graphs of trigonometric functions. Initially, students watched the instructional video before the lesson. In this way, they could imagine and illustrate the graphs of trigonometric functions. The purpose of this study was to describe students’ mathematical communication in discovering concepts of trigonometric function graphs at senior high schools through instructional videos. This qualitative study utilized observation, interviews and student worksheets as instruments. The researchers selected three students as participants and observed and analyzed their written and oral communication skills using rubric guidelines. Students’ worksheets and teacher’s note were used for data analysis. The results of this study found that by watching instructional videos, students were able to master the problems that contained all indicators of mathematical communication. It can be concluded that, generally, students’ mathematical communication skills were considered good. Finally, this study implied that instructional videos could be used as an alternative/innovation in learning to improve students’ mathematical communication skills.
Students’ mathematical reflective thinking ability through scaffolding strategies
Authors: Rahmi N , Zubainur C M , Marwan
Published: March 2020

Mathematics learning at school is not yet sufficient to support students’ mathematical reflective thinking ability. Students’ mathematical reflective thinking ability can be improved by facilitating students with proper learning strategies and giving feedbacks or assistance that can support mathematical reflective thinking. One of the intended strategies is a scaffolding strategy. Scaffolding can encourage and trigger students’ mathematical reflective thinking. This study was qualitative research aiming to describe students’ mathematical reflective thinking ability through scaffolding strategies. The subject of the study was 33 second grade junior high school students. Data for students’ mathematical reflective thinking ability were obtained through three subjects interviewed. Based on the result of the students’ reflective thinking test, each subject was then categorised into high, medium, or the low ability of mathematical reflective thinking. Data obtained from the interview was analysed to describe students’ mathematical reflective thinking ability. The result showed that, in general, students’ mathematical reflective thinking ability through scaffolding strategies were still low. All students achieved the reacting phase of reflective thinking ability; meanwhile, only a few students achieved the comparing and contemplating phase. Further study and research are needed to facilitate a better reflective thinking ability.
Improving students’ problem-solving ability and learning motivation through problem based learning model in senior high school
Authors: Hasrawati  , Ikhsan M. , Hajidin
Published: March 2020

Students’ problem-solving abilities and motivation to learn mathematics is still not as expected. Most students struggle with mathematical problems that are presented in the form of word problems and non-routine problems. The purpose of this study was to determine the improvement of students’ problem-solving ability and learning motivation using PBL model. This study used a quantitative approach with experimental research type. Analyses were carried out using two-way ANAVA test at a significance level of 0.05. The study proves that the improvement of the problem-solving ability of students who were taught using PBL model is better than the students who were taught using conventional learning with a t-count of 2.734 and sig. (1-tailed) = 0.004 (sig ≤ α). The results of the study also showed that the improvement in students’ learning motivation through the application of PBL model was better than those who were taught using conventional learning with a t-count of 1.364 and a sig. value (1-tailed) = 0.0095 (sig ≤ α). PBL model provides positive value for students’ problem-solving skills and learning motivation.
Students’ mathematics problem-solving skills through the application of Problem-Based Learning model
Authors: Suryani I , Maidiyah E , Salasi  , Mardhiah MZ
Published: March 2020

Education aims to foster a variety of potential which exists in human, including the potential to solve any problems, involving solving mathematical problems. This study aimed to describe students’ problem-solving skills during mathematics learning about social arithmetic through Problem-Based Learning (PBL) model. The subjects of this research were 32 Year 7 students in Banda Aceh. This study used a qualitative descriptive approach. Data collection was conducted by problem-solving skills test and interview. The interview data were analysed descriptively, while the data of the test results were analysed based on the assessment guideline of students’ problem-solving skills. It was found that students’ mathematical problem-solving skills through PBL model were already classified as good. High-achieving students of problem-solving skills solved the problem by understanding the problem, planning the solution, doing the solution and rechecking it. Middle-achieving students of problem-solving skills solved the problem by understanding the problem, planning the solution and doing the solution without rechecking it so they get incorrect results. Low-achieving students of problem-solving skills solved the problem by understanding the problem and planning the solution, yet they presented the incorrect solution and did not recheck it. PBL model can be used to improve students’ problem-solving abilities.
Elementary school teacher’s perspectives towards developing mathematics literacy through a STEAM-based approach to learning
Authors: Siregar Y E Y , Rahmawati Y , Suyono
Published: March 2020

This research aimed to investigate teacher’s views about developing mathematical literacy through a STEAM-based approach to learning. The study used descriptive, qualitative data collection through observation, questionnaires, and interviews. The study involved fifth-grade elementary school teachers who have educator certificates and are mathematics expert teachers in Banda Aceh. The results show that the teachers agreed that the STEAM Project should start from an elementary school level of skills necessary for the project, but they did not yet understand the STEAM Project concept as a multidisciplinary learning approach nor they have the knowledge needed to implement it. They argued that the procedural demands during the implementation of the project were confusing, resulting in the achievement of content material taking precedence over developing students’ mathematical literacy. They were also not used to connecting mathematics with the context of everyday life problems, so further guidance for teachers is essential for this approach to be successful. The results of this study are expected to provide teacher’s views about student mathematical literacy and STEAM Project so that it becomes a reference in facing challenges in the global scope.
The metacognitive ability of mathematics education program’s students
Authors: Arnis  , Zubainur C M , Abidin Z
Published: March 2020

The quality of learning outcomes is influenced by the ability of students to control their cognitive processes continuously and develop into metacognitive. Through the metacognitive process, students are expected to be able to motivate, organize, develop themselves to determine their goals and try to achieve the goals to make their learning and thinking processes more effective and efficient. This study aimed to determine the students’ metacognitive abilities of Mathematics Education Study Program in Tarbiyah and Teacher Training Faculty, UIN Ar-Raniry Banda Aceh. The type of research used a quantitative method. The population of the study were all student who took the subject of Field Geometry course in Mathematics Education Study Program, Tarbiyah and Teacher Training Faculty, UIN Ar-Raniry Banda Aceh. The sample was the students in one of the five classes taking the course. The instrument in this study was a problem-solving question. Data analysis was conducted descriptively using Analysis Score. The students have different metacognitive abilities in solving a mathematical problem-solving question. Three of the twenty-three students have high metacognitive abilities.
Students’ learning outcome through the implementation of guided inquiry learning model using Autograph
Authors: Ticaya  , Ikhsan M , Yusrizal
Published: March 2020

Innovations in learning mathematics using technology are needed to improve the students’ learning outcomes. This research aimed to investigate the students’ learning outcome through the implementation of guided inquiry learning model using Autograph in the trigonometric function graph at a senior high school in Aceh Besar, Indonesia. Quantitative approach employing experiment was used in this research. The research samples were Year 10 science students from class X as the experimental class taught with guided inquiry learning model using Autograph, and Year 10 science students from class Y as the control class taught with guided inquiry learning model without using Autograph. The research instruments were pretest and posttest consisting of four essay questions of trigonometric function graph. After the test requirement has been mett-test was conducted with a significant level of 0.05 to examine the improvement of students’ learning outcomes between the experimental and control class. The research findings indicated that the improvement of students’ learning outcome applying guided inquiry learning model using Autograph was better than those without it. The application of guided inquiry learning model using Autograph improved the students’ learning outcome.
Growth inhibition of acacia (Acacia nilotica) with seawater and shade
Authors: Djufri  , Hartini S , Pada A U T , Puspa V R
Published: March 2020

This research investigated the effect of the concentration of seawater (AL) and light intensity on seedling growth of A. nilotica. The research method used a randomized block design (RBD) factorial design with two factors. The first factor is seawater with 6 levels of concentration (AL 0%, 20% AL, AL 40%, AL 60%, 80% AL and AL 100%). The second factor is the shade with three levels of shade (55% NG, NG 65%, and 75% NG). The results showed: (1). Single factor shade and seawater has an inhibitory effect on the percentage of deaths tillers of A. nilotica, each treatment have different effects. The most effective combination inhibited A. nilotica growth was NG3AL5 (shade 75% and 100% concentration of seawater). (2). Seawater treatment affects the tissue organ damage of A. nilotica plants, especially roots and stems, therefore at 14 MST most of the test plants die for not being able to tolerate the treatment, (3). The analysis results of the soil physical and chemical parameters showed that no symptoms of poisoning the soil due to seawater treatment in the test medium, as shown by the high levels of every physical and chemical parameter of land far above the exposure limit.
Ehanol extract from Achantus Ilicifolius L. leaves as anti-inflammatory ulcers in Mus musculus L.
Authors: Rizeki E , Safrida S , Supriatno
Published: March 2020

This study aims to determine the effective concentration of Acanthus ilicifolius L. leaves to treat the number of ulcers and gastric ulcer severity in white mice (Mus musculus L.) which were induced 500 ppm of aspirin. The approach used was quantitative by using a completely randomized design (CRD) factorial pattern with two factors. The results of statistical analysis of the number of ulcers for factor A (source) show the value of Fcount>Ftable = 15>3.16, while factor B (concentration) shows the value of Fcount>Ftable = 64> 2.38 at the test level α 0.05. While the results of statistical analysis of factor A ulcer severity (Source) shows the value of Fcount > Ftable = 4 > 3.16 and factor B (concentration) Fcount > Ftable = 38 > 2.38 at the test level α 0.05. The highest percentage of treatment was at the concentration of P2 (250,000 ppm) and P3 (500,000 ppm) at 17%. The ability of gastric ulcer healing in mice is because the parts of the Achantus ilicifolius L contain several phytochemical compounds including flavonoids, saponins, alkaloids, phenolics, and terpenoids. Therefore, it can be concluded that the Achantus ilicifolius L leaves with a concentration of 250,000 ppm are effective in treating the number of gastric ulcer and ulcer severity in mice.
Content validity of conation assessment
Authors: Muliana N , Pada A U T , Nurmaliah C
Published: March 2020

Content validity reflects the representation and relevance of a set of items used to measure a concept carried out through rational analysis of the contents of the test through expert judgment. This validity refers to the accuracy of the measurement based on the contents of the instrument to ensure that the scale items used have met the overall concept content or suitability of the items. The purpose of this study is to evaluate the validity of the contents of the Conation-Based Instrument in High School Biology Learning and explain the procedure for calculating the coefficient values of each instrument item. Five experts validated the instrument in this study consisting of three biology education experts and two psychology experts. The assessment criteria assessed were based on construction and clarity, which are assessed on a 4-point scale. Data collection was carried out using the Aiken V. formula. The result, from 39 items, which had a content validity coefficient greater than 0.87 (26 items) and the rest (12 items) were discarded.
The implementation of project-based learning model to improve students’ learning outcomes
Authors: Syukriah S , Nurmaliah C , Abdullah A
Published: March 2020

This study aims to determine the improvement of students’ cognitive learning outcomes after the implementation of the PjBL model combined with PDEODE (predictdiscuss-explain-observe-discuss-explain). This research was conducted at several senior high schools in Banda Aceh from September to October 2018. The Pre-test post-test control group design was employed in this research. The total of 172 students was involved as subjects which was divided in to 2 groups (control and experiment). The students’ learning outcomes were measured by calculating gain normalization (g) and the Mann-Whitney test. The results showed a significant difference between control group and experiment group α =0.05, the value of Asymp. Sig. (2-tailed) 0.00<0.05. To concluded, the PjBL model combined with PDEODE has significantly improve the cognitive learning outcomes of students.
The implementation of problem-based learning module to improve the students’ learning outcomes and perceptions
Authors: Auly R , Mudatsir  , Evendi
Published: March 2020

The present study aims to increase students’ learning outcomes and to explore their perceptions of problem-based learning module of living organisms at MTsN 2 Aceh Jaya. The pre-experimental method with the one-group pretest-posttest design was used, and the purposive sampling technique was employed. The collection of data was performed on May–June 2019, and the study was conducted in class VIId. The research instruments include multiple-choices tests totaling 20 items and students’ perception questionnaire. For the data analysis, the N-Gain was utilized to see the improvement in learning outcomes and the t-test to examine the hypothesis of the implementation of PBL module. The Likert scale was used to analyze the data of student perception questionnaire. The results indicate that the implementation of the PBL-based module of living organisms interaction can improve the student learning outcomes with an N-Gain value of 0.57, which means the medium category, and tscore > ttable (14.92 > 2.042), and the students’ perceptions on the implementation of the PBL-based module of living organisms interaction are obtained to be an average of 60%, which is considered in good category.