bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effects of Wind Veer During the Morning and Evening Transitions
Authors: Sanchez Gomez M , Lundquist J K
Published: March 2020

Direction and speed wind shear modify turbine performance by changing inflow conditions on turbine blades. Using observations from the 2013 CWEX campaign, we found the daily atmospheric boundary layer transitions (morning and evening) match periods of high electricity demand for a wind farm in central Iowa. Power production during these periods was undermined for large direction shear and low speed shear scenarios. The morning transition displayed larger direction shear over the rotor layer for most wind speeds compared to the evening period. This resulted in lower turbine performance for the morning compared to the evening and whole day. This study shows that the combined effect of direction and speed shear is affecting turbine operation during high electricity demand times of day, predominantly during the morning at this wind farm.
Lidar Scanning of Induction Zone Wind Fields over Sloping Terrain
Authors: Mikkelsen T. , Sjöholm M. , Astrup P. , Peña A. , Larsen G. , van Dooren M. F. , Kidambi Sekar A. P.
Published: March 2020

Scanning lidar instrumentation in the form of two synchronized ground-based WindScanners and a nacelle-mounted SpinnerLidar were deployed to measure wind fields in a vertical plane in the induction zone from 0.2 to 3 rotor diameters (D) in front of a Vestas V52 test turbine (D = 52 m) situated at DTU Risø Campus. First, the two ground-based WindScanners accurately reproduced the vertical profiles of horizontal mean wind speed when compared to measurements from a reference met-mast installed 2.2D upwind in the prevailing wind direction. The vertical plane scanned wind field measurements within in the induction zone were also compared with the vertical wind profiles measured at 1D by a nacelle-mounted scanning DTU SpinnerLidar. The vertical plane wind field as measured by the two ground-based WindScanners shows that the wind field in the induction zone is influenced by the turbine as well as by the slope of the terrain.
Simulating the response of a small horizontal-axis wind turbine during wind gust using FAST
Authors: Rakib M I , Evans S P , Clausen P D
Published: March 2020

Small wind turbines are often erected close to their load and in urban environments where the mean velocity is lower than ideal, and the turbulence level is high. Urban environments experience wind gust events with published results indicating the number of gust events greatly exceeding the number of events assumed in International Electrotechnical Commission (IEC) standard IEC 61400.2-2013. The work presented in this paper has analysed detailed wind measurements from a site in an urban environment to identify wind gust events and has used these wind gust events as an input into a FAST model of a 5 kW horizontal-axis wind turbine. The FAST model predicted the turbine power extracted from the measured gusts are lower than the power extracted during the assumed IEC gust. The maximum energy extracted by the turbine during a gust is at least 36% lower for a measured gust event than for the assumed ideal gust event.
Improvement of wavefront correcting particle swarm optimization algorithm based on metropolis criterion
Authors: Hao Shiqi , Xu Chenlu , Chen Yang , Liu Zehua
Published: March 2020

In this paper, the wavefront phase distortion of laser beam caused by atmospheric turbulence is corrected by an adaptive optics sensor-less system. According to the shortcoming that particle swarm optimization algorithm(PSO) is easily falling into local optimum, an improved PSO algorithm based on metropolis criterion is proposed in order to improve the efficiency and effect of wavefront correction. The metropolis criterion is used to judge whether an inferior state is accepted or not, so that the particle swarm optimization algorithm can jump out of the local optimum. Finally, the correcting effect of the improved PSO algorithm is compared by numerical simulation analysis, which verifies that the improved PSO algorithm can improve the correcting speed and effect, and the running time of achieving the same correcting effect is greatly reduced.
Improvement of Faster-RCNN Detection Algorithms for Small Size Line Accessory Equipment
Authors: Qi Wang , Zhenli Wang , Jianxiang Li , Yuechen Yang , Piyu Liu
Published: March 2020

Aiming at the problem that the image of patrol inspection is affected by angle and distance, the target of small size accessory equipment in the image is small, and the recognition rate of current algorithm is low. An improved Faster-RCNN detection algorithm for small size accessory equipment of patrol image is proposed. By adjusting the structure of Faster-RCNN network, we can overcome the shortcomings of the current algorithm. Firstly, the deep residual network ResNet50 is used to replace VGG16 network. At the same time, the bottom and high-level features of convolution network are applied to the selection of candidate regions, so as to improve the utilization of effective information of targets, and then to improve the detection precision of small-sized targets. The results show that the detection accuracy of the improved Faster-RCNN model is 4.2% higher than that of the original algorithm.
Fast and coarse registration of point cloud in ICP three-dimensional space
Authors: Ren Xiaokang , Zhang Mai , Wang Wenqiao , Mao Xuetao , Ren Jie
Published: March 2020

The traditional ICP algorithm has a large amount of computation in the process of searching for corresponding points[1].This is the bottleneck of the traditional ICP algorithm[2],and the standard ICP is looking for the corresponding point[3].The point where the Euclidean distance is closest is the corresponding point[4].Unreasonable,there will be a certain number of wrong points[5].In order to improve the accuracy and speed of 3D point cloud data registration,this paper combines the coarse registration of the center of the box and the circular domain search method of the rasterized small cube to further improve and optimize the traditional ICP algorithm[6].
A fourth-order 9-point finite difference method for the Helmholtz equation
Authors: Chen Jianjun , Cheng D S , Jie Rong , Zhu Xiaoling
Published: March 2020

In this paper, we develop a new fourth-order 9-point finite difference scheme for solving the Helmholtz equation. The central fourth-order difference scheme is used to discretize the second-order derivative, and the matched interface and boundary (MIB) method is employed to deal with the resulting boundary problem. For the discretization of the zero-order term, a weighted average method is designed by utilizing all of the 9 points, and the weight parameters are determined by minimizing the numerical dispersion. The new method is simple and efficient in suppressing the numerical dispersion. Finally, numerical examples are presented to illustrate the numerical convergence and effectiveness of the new scheme.
Research on Digital Image Correlation Method used for Strain Detection
Authors: Tao Li , Yang Zaihua , Yi Wangmin , Duan Chenxu , Zhang Yuhang , Wang Ziwen
Published: March 2020

Strain measurement is an important means to study structural strength. Strain gauge measurement method is widely used in different industry area. The strain gauge should be stuck on the surface of the measured object to detect displacement or strain. As the measurement mode is contact measurement, strain gauge can have a certain impact on the measured object itself. So research of strain detection is increasingly moving from contact method to non-contact method. In this paper, we design a digital image correlation non-contact method used for strain detection. The basic principle of image correlation method is introduced. And the image process algorithm is detailed described. A set of laser lighting hardware is especially used for object illumination to get a random distribution light spot instead of sticking target bar on object. The measurement results show that this method can detect strain with good effect and can be applied to non-contact strain detection test.
Suggestions and thoughts on improving the teaching ability of teachers in higher vocational colleges under the background of MOOC
Authors: Guangyong Liu , Ruolan LI
Published: March 2020

This paper elaborates on the emergence and development of MOOC, investigates and researches the current situation of teachers’ MOOC teaching in higher vocational colleges, deeply analyzes the role of MOOC in promoting the professional knowledge structure and professional quality of teachers in higher vocational colleges, and puts forward some Suggestions and thoughts on the current situation of teachers’ teaching ability in terms of using MOOC in higher vocational colleges. The first section in your paper
Unmanned Vehicle Route Planning Based on Improved Artificial Potential Field Method
Authors: Zha Min , Wang Zhiwen , Feng Jing , Cao Xinliang
Published: March 2020

Aiming at the defect of target unreachable and local minimum in the unmanned path planning by traditional artificial potential field method, an improved artificial potential field method is proposed in this paper. By rotating the repulsive force by a certain angle, the unmanned vehicle jumps out of the local minimum point, and adopts the safety distance and the adjustment factor in the repulsive field to solve the problem of the target unreachability, and also ensures the safety of unmanned vehicles during obstacle avoidance. The experimental results show that the improved artificial potential field method can better achieve the safe obstacle avoidance of driverless vehicles and effectively plan the optimal target path.
The application of artificial intelligence in stock investment
Authors: Gao Zhao
Published: March 2020

This paper mainly studies the application of artificial intelligence and machine learning in the field of stock investment. The principles and characteristics of KNN, k-Means, bisecting k-Means, and ANN algorithms are studied to compare the effects, similarities and differences of different algorithms. The algorithms are implemented through Python programs for stock analysis. According to the P/E ratio, dividend rate, fixed asset turnover rate, gross profit margin and other indicators of each stock, the stocks are classified and clustered to predict the stock development prospects and provide reference for selecting appropriate investment strategies.
Design of Virtual Home Decoration Indoor Scene Automatic Reconstruction System Based on Three-dimensional Vision
Authors: Libin Tong
Published: March 2020

Because indoor scenes are structured, even small errors can lead to obvious visual differences in image-based indoor scene automatic reconstruction. Aiming at the specific requirements of automatic indoor scene reconstruction in virtual home decoration, this paper designs an automatic indoor scene reconstruction system based on image, including the calibration of image acquisition platform, the matching and reconstruction of feature features and feature lines, and the fusion of reconstruction results from multiple perspectives. In the overall optimization of reconstruction results, feature features and feature lines are integrated. The system is convenient and practical, and can get better reconstruction results.
FM-to-AM Effect Removal Technology of Pulse Waveform Data Based on Deep Learning
Authors: Tian Zhiyu , Zhang Hao , Chu Xinkun , Qin Ying , Zhang Qing , Geng Yuanchao , Huang Xiaoxia , Wang Wenyi , Wang Fang
Published: March 2020

In the process of laser pulse transmission, amplification and sampling measurement, the amplitude-to-frequency modulation (FM-to-AM) effect caused by spectral distortion leads to peak oscillation of waveform measurement data, which is difficult to be completely eliminated by improving measurement methods. In this paper, a method of removing the FM-to AM effect of pulse waveform data based on deep learning is proposed. After the original waveform data is de-modulated, the accuracy of waveform prediction is obviously improved. This technology can precisely remove modulated signals while retaining the key features of original data, and can well deal with various complex waveform data.
Research on binary vulnerability mining technology based on control flow integrity detection
Authors: Zhang Bing
Published: March 2020

Aiming at the problem of strong pertinence and poor universality of existing binary vulnerability detection methods, binary vulnerability automatic mining method based on control flow integrity detection is proposed. In view of the common binary vulnerabilities, such as stack overflow, heap overflow, function pointer tampering, structured exception handling attacks and so on, they can be unified and abstracted to destroy the integrity of control flow. This method first obtains all legitimate control flow transfers through static binary analysis, then uses symbol execution engine to explore all feasible paths, and injects constraints at control flow transfers. Finally, by solving constraints to determine whether there is a vulnerability that destroys the integrity of control flow. If there is, it automatically generates input that can trigger vulnerabilities.
Security mechanism building for big data based on Trusted Computing
Authors: Hai-yan Lv , Li-Jun Zhou , Yuan Zhao
Published: March 2020

In view of the security risks faced by large data in the process of collection, storage, transmission and use, the security threats faced by large data in these processes are analyzed. The characteristics of trusted computing and its advantages in information security protection are analyzed. On this basis, a secure storage, transmission and authentication mechanism of large data based on trusted computing is proposed to achieve active network defense and ensure that large data can be controlled, trusted and manageable in the process of storage, transmission and authentication.
Short-term electricity price forecast based on long short-term memory neural network
Authors: Lihong Dong , Qian Xie
Published: March 2020

Effective short-term electricity price forecasting is of great significance to reduce bidding risk and obtain stable income. In order to predict the timing price, this paper proposes a short-term electricity price forecasting model based on Long Short-Term Memory (LSTM) neural network. The model uses pauta criterion and Lagrange interpolation polynomial to process the historical data of electricity price, normalize the obtained data and input it into the LSTM network layer, using the Mean Squared Error as the loss function to measure the model forecasting. The effect is based on the loss function, and the weight coefficient of the LSTM neural network is updated by the Adam algorithm. Finally, the timing of the LSTM neural network is predicted. Using the real-time data of the California power market and the real-time data of the US PJM power market, it is proved that the accuracy of this method is higher than that of BP neural network, Cart regression tree and polynomial regression.
Modulation Recognition of 5G Signals Based on AlexNet Convolutional Neural Network
Authors: Zhang Qing , Hu Guobing , Zhao Pinjiao , Yang Li
Published: March 2020

Aiming at the problem that signal modulation recognition under a non-cooperative condition requires substantial a priori information of the signal and a complex artificial selection of the features, this paper proposes a modulation recognition method for the 5th-generation (5G) signal modulation based on the AlexNet convolutional neural network. For the five commonly used 5G signals (3GPP R15 protocol recommendations) of π/2-BPSK, QPSK, 16QAM, 64QAM, and 256QAM, the constellation is selected as input feature of the AlexNet network to construct the recognition classification algorithm. The simulation results show that the average recognition accuracy of the five commonly used 5G signals is up to 90% under a 15 dB signal-to-noise ratio (SNR), an improved performance compared with that of the existing recognition algorithms based on signal scatter plots.
A Practical Method to Improve Absolute Positioning Accuracy of Industrial Robot
Authors: Shi Guosong , Zhao Shuying , Hu Baolong
Published: March 2020

Industrial robots had been wildly used in modern factory. While the high repeatability of industrial robots can address most of need of process, their low absolute positioning ability cannot reach the demands of some high precision tasks. An example is if the action of the robot relies on the real-time sampled data rather than the simulated data. The low absolute positioning ability of the robot will cause working performance far different away from expectation. For improving the absolute positioning accuracy as long as compile with the programs of automation integration projects, a practical general compensation method has been advanced. Our method also can be a compensation frame which can absorb new better model in the future. One application of this method in a project verifies the achievement of this method.
A Survey on Image Style Transfer Approaches Using Deep Learning
Authors: Zhao Changshen
Published: March 2020

Image style transfer has been an important branch of computer vision and image processing. Inspired by the development of deep learning, applications of Convolutional Neural Networks (CNNs) in recomposing content and style of two separated images were proven to be effective by the recent works of image style transfer. This paper provides a survey of image style transfer, which focuses on methods which using deep learning. In addition, this paper also provides information of methods without deep learning, and seeks their points of innovation. In this paper, all references are published ranging from 2001 to 2017, which presents an overview of progress in this realm over the past two decades.
FMN: Two-stage web image fine-grained text detection method based on full convolutional network
Authors: Fan Jiajie , Wang Yongli , Sun Shurong
Published: March 2020

In the Internet, there are large-scale web images with different image sizes and different resolutions. In order to accurately and efficiently identify fine-grained texts in web images, this paper uses FCN to support the ability to semantically segment images, and treats text and background as different detection targets. A two-stage web image fine-grained text detection method FMN is proposed. For the problem that the output result of the whole convolutional network is not fine enough, the MSER+NMS algorithm is used to extract the fine-grained features of the FCN output, and the ellipse fitting method is introduced to detect the text. Features such as character tilt and morphological differences ultimately result in text detection of the image. Compared with the existing methods, the FMN algorithm proposed in this paper has achieved certain improvement in detail resolution and precision.
Construction of vehicle driving cycle in Fuzhou
Authors: Zou Sijia , Zhu Xinru , Xu Linjun , Zhu He
Published: March 2020

The current driving cycle in Europe is not consistent with China’s actual road conditions, and the traffic conditions in different cities of China also are discrepant. Therefore, it is increasingly urgent to build corresponding driving cycle based on the driving data of each city. In this paper, based on noise reduction pretreatment of vehicle driving data in three areas of Fuzhou city, K-means clustering is carried out on the first five principal components of the extracted kinematic segments obtained by principal component analysis. Then, three types of kinematic segments, which represent relatively smooth traffic, traffic congestion and smooth traffic, are obtained respectively. So the vehicle driving cycle is constructed, and the correlation coefficient between it and the sampling population for constructing is calculated. Finally, the error analysis method and correlation coefficient are used to verify the constructed driving cycle that is rational and representative.
Analysis of the Influence of Digital Media Technology on Music Communication
Authors: Wenting Kong
Published: March 2020

Under the condition of digitalization, mass media, as a carrier, has come into people’s vision, and has exerted an important influence on music communication. The media industry has grown steadily, and the relationship between mass media and music has gradually become complex and close. With the continuous development of the media industry, it has an impact on the production, dissemination and popularity of music art. This paper mainly discusses the role of the mass media and the influence of the mass media on music communication, and analyses the ways to improve the mass media on music communication.
MIHNet: Combining N-gram, Sequential and Global Information for Text Classification
Authors: Song Yingxin
Published: March 2020

Pre-training language model has achieved amazing results in many NLP tasks. In Particular, BERT (Bidirectional Encoder Representations from Transformers) create a new era in NLP tasks. Despite the success, these model perform well at Global Information but weak on n-gram and Sequential information. In this paper, we conduct exhaustive experiments of classical text classification models upon BERT in text classification task and provide a general guide for BERT+ models. Finally, we propose a new text classification model called MIHNet (Multi-dimension Information Integration using Highway network), which integrates Global, n-gram and Sequential information together and get a better performance. Notably, our model obtains new state-of-the-art results on eight widely-studied text classification datasets.
Clustering Analysis of Urban Fabric Detection Based on Mobile Traffic Data
Authors: Liu Chuangfei , Liu Chang , Liu Fuqiang , Hu Jianyao
Published: March 2020

The rapid development of city makes it complicated to analyse urban structure. It’s difficult to learn city ecosystem using traditional methods including interview and survey. A city generates lots of data every day, which could tell city’s dynamic fabric. The moving patterns of citizens could be illustrated through analysing traffic data on their mobile phones, since those who carry a mobile phone possess a large percentage of the urban population. Thus, we can get the designated urban area’s functions through the moving pattern analysis. In this paper, we explore the fabric of city using cluster analysis based on deep learning with mobile phone communication data. We get the inspiration from image processing and build communication snapshot map to represent each region. After extracting features using deep learning method, we use unsupervised learning to find similar regions of the city. The clustering analysis result is examined by the ground truth data.
Statistical downscaling of GCM using kernel support vector regression for rainfall prediction in Bireuen district
Authors: Sanusi  , Husna J
Published: March 2020

Global climate phenomena, including El-Nino and Southern Oscillation, Indian Ocean Dipole and Madden Julian Oscillation, have affected rainfall behavior in Indonesia. This study attempts to model a statistical downscaling of the General Circulation Model (GCM), which is usually used as learning data for the prediction of climate change, using Support Vector Regression (SVR) to predict rainfall during the dry season in Bireuen, Aceh Province. Data consists of observational rainfall data at 7 (seven) weather stations and 10 models of 7x7grid-scale hindcasts of GCM data collected within 24 years (1990-2013). The RBF kernel was found to produce the best performance (correlation value: 0.828; RMSE: 24.035) compared to the linear (correlation value: 0.538; RMSE: 27.207) and polynomial kernel (correlation value: 0.639; RMSE: 25.584). Among GCM downscaling output, CMC1-CanCM3 was considered to be the best model for rainfall prediction in Bireun District, particularly in Peudada and Gandapura regions. This model is recommended to be used as a rainfall forecasting tool during drought and would help in optimizing the arrangement of cropping strategy in Bireun area to prevent possible crop failure.
Analysis of the needs of science, technology, engineering and mathematics (STEM) learning instruments on the derivative topic
Authors: Husna R , Johar R , Usman
Published: March 2020

STEM (Science, Technology, Engineering, and Mathematics) is an approach which combines or fuses science, technology, engineering, and mathematics into education program to improve students’ learning potential. STEM is started to be disseminated by the government to provide a push for the realization of Industrial Revolution 4.0 in this century. The purpose of this study is to describe the necessity of STEM learning instruments on the derivative topic. This study is part of the developmental research on the preliminary stage, which is the needs analysis. Three of eleventh-grade math teachers were invited to participate in this study. The questionnaire about various aspects of the needs analysis was given to the teachers. The study results found that the teachers agree to develop STEM learning instruments on the derivative topic and are interested in examining the instruments if they are accessible for them.
Need analysis for developing Geogebra assisted mathematics learning tools to improve mathematical understanding of senior high school
Authors: Handayani V , Usman  , Ansari BI
Published: March 2020

The aim of this study is to analyze the initial needs of the development of GeoGebra assisted mathematics learning tools to improve students’ mathematical understanding abilities. The population of this study were students from SMA Negeri 6 Banda Aceh and Mathematics teachers from four schools in Banda Aceh and Tanjung Pura. Purposive sampling method was used to take the sample. Data collection were collected by observation and interview. All the data were analyzed descriptively. The findings revealed that mathematics learning in 11th grade high school students can be developed with the help of GeoGebra. Students feel interested and motivated to take part in assisting GeoGebra. It also found that GeoGebra assisted mathematics learning tools are needed to improve students’ mathematical understanding because the students’ mathematical understanding ability is still low.
The quantitative reasoning ability of high school students
Authors: Hafiza N , Usman  , Anwar
Published: March 2020

Quantitative reasoning is a mental activity that emphasizes drawing conclusions based on quantitative data or information. Students are often wrong in solving quantitative reasoning problems. Students do not understand very well the relationship between the information provided with the questions in the problem. The solution to this problem is to investigate students’ quantitative reasoning ability. This study aims to find out the activities of students’ quantitative reasoning abilities. This research uses a qualitative approach. Twenty-three high school students were involved in this study. Data is collected by giving tests in the form of quantitative reasoning ability questions. After the test, a brief interview was conducted with some students. The results of this study indicate that students still have difficulties in answering questions in the form of quantitative reasoning abilities. Therefore serious attention is needed to improve students’ quantitative reasoning abilities.
Development of student worksheet to improve mathematical representation ability using realistic mathematics approach assisted by GeoGebra software
Authors: Nurlisna  , Anwar  , Subianto M
Published: March 2020

Nowadays, student worksheet is used with a lack of concern on students’ mathematical representation ability. In fact, it is essential to encourage students to deliver their mathematical ideas in various ways for broader perspectives of mathematical problem-solving. This study aimed to generate a valid, practical, and effective student worksheet to improve students’ mathematical representation ability with a Realistic Mathematics approach assisted by GeoGebra Software. The research is developmental research using the ADDIE model, which includes Analysis, Design, Development, Implementation, and Evaluation. The instruments used in this study included: assessment sheets (to measure the validity of the worksheet); student questionnaire; teacher questionnaire; observation sheets (to measure practicality of the worksheet implementation); and mathematical representation ability test (to measure the effectiveness of worksheet implementation). This study created a student worksheet for four meetings. The student worksheet met valid criteria, with an average score of 4.25. The practical criteria were indicated by student and teacher questionnaires, with an average of 4.25 and 4.4, respectively; and the learning implementation (81.72%). Furthermore, the learning material fulfilled the effectiveness criteria with the percentage of students’ learning understanding was 85.71%.
Students’ mathematical understanding ability using contextual teaching and learning approach
Authors: Nufus M , Marwan  , Zubainur C M
Published: March 2020

Students’ mathematical understanding ability is one of the students’ compulsory competences in learning mathematics. In the learning process, students commonly hesitated to ask the teacher how to solve mathematical problems even though they did not understand the lesson. This research aimed to improve the mathematical understanding ability by applying Contextual Teaching and Learning (CTL). This study employed a qualitative approach using True Experimental Design. This paper explained that the experimental result of students’ mathematical understanding ability used Contextual Teaching and Learning (CTL). The test of mathematical understanding ability was administered to Year 7 students of one of the junior high school in Sigli, Pidie. The instrument was a mathematical understanding ability test. The data analysis showed that the result of the normality test scores of the students’ mathematical understanding ability of the experimental class and the control class were 0.288 and 0.125. This research found that learning by using CTL indicated to improve the students’ mathematical ability. Mathematics teachers can apply CTL in the learning as an effort of developing students’ mathematical understanding ability in other algebra lessons.
Asian Elephants: 15 years of research and conservation
Authors: Hankinson Emma , Nijman Vincent , Abdullah
Published: March 2020

This document provides an overview of the research and conservation work that has been undertaken to date by our team at Oxford Brookes University in regard to Asian elephants. Research began in 2006, in collaboration with various NGO’s from a range of countries (Indonesia, Malaysia, Myanmar), government agencies (Indonesia, Myanmar, Thailand, Sri Lanka) and local and international universities. Four major themes are recognised throughout our research; (1) the trade in live elephants, (2) the trade in elephant ivory and other body parts, (3) elephant distribution and abundance in Northern Sumatra, and (4) the wider conservation agenda of elephant conservation in Asia. The live trade in elephants concerns both the domestic trade (Indonesia, Thailand, Sri Lanka) and the international trade (Laos, Myanmar), and has a strong welfare component that needs to be addressed. The trade in ivory is still prevalent in Myanmar, Laos and Thailand, all with strong links to China, whereas the trade in ivory in Indonesia is more localised. The distribution mapping of elephants has so far been undertaken in Aceh and North Sumatra, and we will continue within this area of research in collaboration with Universitas Syiah Kuala. With respect to the wider conservation agenda of elephant conservation in Asia, many regions and most countries face similar opportunities and challenges. Lessons learned in countries such as Myanmar, Sri Lanka and Thailand, all with substantial elephant populations, need to be employed in Indonesia to better the conservation status of elephants and improve the lives of people that live side by side with the Asian elephant.
Potential of three aquatic plants as phytoremediator for liquid waste of tofu
Authors: Wardiah  , Supriatno  , Amalia A , Andayani D
Published: March 2020

Phytoremediation is a method of using plants to remove pollutants from contaminated soil or waters. Several species of aquatic plants have been used in reducing pollutants in the waters. The purpose of this study was to determine the effectiveness of three species of aquatic plants as phytoremediators to reduce levels of COD and BOD of liquid waste, the effect of tofu liquid waste on the fresh weight of the species, and the chlorophyll of the plants content after treatment. Data obtained through experimental methods through a quantitative approach with non-factorial Complete Randomized Design. Data analysis used Analysis of Variance. The results showed that three types of water plants were effectively used as phytoremediator in tofu waste water with the highest percentage of BOD and COD reduction obtained in a combination of Water lettuce (Pistia stratiotes L.) and Yellow velvetleaf (Limnocharis flava L.) which was 99.42%. Besides, liquid tofu waste is effective in an increase in the species chlorophyll content. In brief, the aquatic plant species are effective to reduce COD and BOD levels and to increase of chlorophyll content and fresh weight of the species.
Guided note taking based on students worksheet effect towards students learning outcome
Authors: Febriana E , Hasanuddin  , Huda I , Supriatno  , Sarong M A
Published: March 2020

This research aims to discover the effect of students worksheet based on guided note taking towards the learning outcome of students both individually and as a group. The method implemented in this research is Quasi-experimental research with the Nonequivalent Control Group Design of 138 population and 82 students as samples. Instruments used in this research are multiple-choice questions to measure the learning outcomes, syllabus, learning implementation plan, and learners’ worksheets. The data collection process was conducted by looking for the post-test and pre-test values as well as t-test to compare it to pre-test and post-test scores. Data analysis was conducted using an Independent Sample T-test testing at a significant level of 0.05. The result of Independent Sample T-test testing at the outcome of control class indicates that the outcome (Mean=77,48, Standv=4.830) is different from the value in experiment class (Mean=84.93, Standv=3.672), tcount= 7.89 > ttable 1.66 at the Sig. (2-tailed) level of 005. It can be concluded that students who use the guided-note-taking based learners’ worksheet as a group show a better learning outcome compared to the students who use guided-note-taking based learner worksheets individually at Ingin Jaya High School Aceh Besar.
Preparation and characterization of jatropha curcas leaves as a biosorbent for Pb(II) and Cd(II) removal in liquid waste
Authors: Zubir M , Muchtar Z , Mahmud  , Nasution H I , Jasmidi
Published: March 2020

This study aims to determine the capacity of Jatropha leaves to absorb Pb (II), Cd (II) and their mixture in liquid waste. Jatropha leaves were obtained from Durian, Labu Beach, North Sumatra, heated for 4 hours and 24 hours, then characterized by XRD, FTIR, and SEM. XRD and FTIR spectra confirmed there are no structural differences of Jatropha leaf biosorbent with 4 hours and 24-hour pretreatment. SEM images indicate that the surface of the biosorbent prepared for 24 hours is smoother than the 4-hour preparation. The number of Jatropha leaves which absorbed Pb (II) and Cd (II) were measured with AAS and the optimum concentration of 0.5 gram of adsorbent for Pb (II) is 80 ppm (90 minutes) adsorbed up to 95.52%. It is higher than the absorption of Cd(II) which is 76.37% at a concentration of 100 ppm (90 minutes). But the mixture of Pb (II) and Cd (II) induce the opposite result. Cd (II) was absorbed more by 92.48% while Pb (II) was 88.62% at a concentration of 100 ppm for 90 minutes. Although Pb (II) can be absorbed more than Cd (II), when the two heavy metals mixed, Cd (II) is absorbed more than Pb (II).
Synthesis of ZIF-8 at room temperature and reuse filtrate
Authors: Jofrishal  , Fajri R , Ediati R , Amri Y
Published: March 2020

ZIF-8 has been synthesized with methanol solvent at room temperature (25-30°C), with molar composition of zinc nitrate: 2-methylimidazolate: TEA = 1: 4: 4. Furthermore, ZIF-8 has been synthesized using filtrate from synthesis. Repetition of ZIF-8 synthesis using filtrate from synthesis has been done for six times. The produced samples were investigated using XRD, FT-IR and SEM. XRD pattern showed that ZIF-8 which have been synthesized using reuse filtrate has same peaks with ZIF-8 from first synthesized. FTIR spectra of ZIF-8 showed a peak at 422 cm−1 which indicated the presence of zinc bonded to the nitrogen in ZIF-8. Besides SEM analysis of ZIF-8 particles size whit are synthesized using MeOH and reuse filtrate have yielded that ZIF-8 micropore size. The results showed that the synthesis of ZIF-8 using reuse filtrate was succesfully carried out.
The effectiveness of an interactive module in improving students’ conceptual understanding of acid-base titration
Authors: Desriana D , Khaldun I , Maulana I , Habibati  , Ismayani A
Published: March 2020

The results of the previous studies showed that the implementation of acid-base titration module with interactive media which is based on computer technology can assist students to comprehend the concepts taught in the subject. In this study, the module with interactive media was delivered as an alternative in the real practicum for the material of acid-base titration. This study was conducted using a quantitative research method. The sample was collected by purposive sampling and divided into the experiment and control class. Data were analysed by the normalized gain (N-gain) while the student concept understanding data analysed by using percentage techniques. Data was collected through pre-test and post-test in both classes. Based on the result of N-gain average, the improvement of concept comprehension at the control class is 0.50 while it is 0.79 at the experiment class which is in the medium level. The hypothesis test of these two classes shows that the t-score (7.405) > t-table (1.996) with a significant level of 0.05. It can be concluded that there was a significant difference between control class and experimental class. The implementation of the module is effective in improving students’ comprehension of the concept.
Developing Problem-Based worksheet to improve students’ critical thinking skills and learning outcomes in the concept of chemical bonding
Authors: Munawarah M , Haji A G , Maulana I
Published: March 2020

This study aims at developing worksheet on problem-based chemical bonding material to determine quality, critical thinking ability and learning outcomes on the worksheet. Research and development (R & D) was used in this study. The population in this study were all students at X science class at senior high school Banda Aceh, Indonesia in 2018/2019 academic year. Sample selection in this study was done using random sampling with students at X science2 class and X science3 class. Data were analyzed using N-Gain test and t-test after the test showed that problem-based worksheet can improve student’s critical thinking skill (CTS). This can be seen from the N-Gain value from each indicator with the high category; and it was also strengthened with the Z test on each t-test indicator that showed the value of ttest = 5.08 and ttable= 1.68. It can be concluded that there are differences of the increase students’ learning outcomes between experimental class and control class through the development of problem-based worksheet on chemical bonding material.
Effectiveness of a question web-based learning model to improve students’ science process skills
Authors: Evendi  , Susanna K , Nurulwati
Published: March 2020

This study aims to improve students’ science process skills (SPS) through the use of question web based learning model (QWBL) in physics learning. The QWBLis a learning model specifically designed to train students’ scientific questioning skills. Scientific questioning skills are related to SPS because every scientific question must be proven through experiments. This research employed educational design research (EDR) and was conducted at senior high schools in academic year of 2017/2018 involving 32 students as samples. This study used one group pre-test and post-test design and data was analyzed descriptively using the n-gain indicator. The results showed that only 3.125% of students completed pre-test and 87.5% of students completed at post-test. The increase of n-gain score was from 0.33 to 0.88 which fall into moderate and high category. The increase in N-gain indicated that the QWBL model was effective to improve SPS in physics learning especially in subject of static fluid. This criterion shows that the QWBL is effective for improving students’ science process skills in learning of physic.
Teachers’ perspectives toward soft skills in science learning
Authors: Susilawati  , Aznam N , Paidi  , Ngadimin
Published: March 2020

Soft skills are the essential attribute which has significant impacts on students’ achievement. This research aimed to investigate science teachers’ perception related to the importance of soft skills for the student, the components of soft skill needed in science learning, the appropriateness method in integrated soft skills in science learning and the challenges of soft skills improvement in science learning. By the purposive sampling, 100 science teachers from junior high schools were investigated. Survey method was used to collect the data and the analysis was conducted using descriptive-statistical analysis. The result showed 34% science teachers’ believe soft skills are critical factors for productive performance. Twelve components of soft skills which were needed in science learning are critical thinking, teamwork, creative and innovative, communication, problem solving, curiosity, rigorous, integrity, analysis, research and inquiry, information literacy, and objective. Data express the percentage difference in each component of soft skills. The finding revealed embedded and integrated are the significant methods in enhancing students’ soft skills. Lack of teachers’ understanding and learning resources and school principals’ support become the challenges in developing students’ soft skills. The study recommends developing an appropriating model or strategy in promoting soft skills.
Level of teachers’ knowledge and understanding in developing test questions
Authors: Yusrizal  , Halim A , Daud M , Saminan
Published: March 2020

This study aims to describe the level of knowledge and understanding of state junior high school science teachers in compiling exam questions. This research is a descriptive study with a quantitative approach. The method of data collection was carried out through a survey using the developed questionnaire instrument. The population in this study were all state junior high school science teachers in Pidie District, and 47 teachers were randomly selected to be samples from 60 state junior high schools. The data were analyzed descriptively. The results showed that 67% of science teachers had a very high level of knowledge in preparing exam questions, and 50% had a high level of understanding in preparing exam questions.
Sea-level rise allowances for the UK
Authors: Howard Tom , Palmer Matthew D
Published: March 2020

We evaluate sea-level-rise allowances for UK tide-gauge locations based on the best available current description of present-day extreme still water return level curves and the United Kingdom Climate Projections 2018 (UKCP18) regionalised projections of future time-mean sea-level (MSL) change. We focus primarily on projections for 2100 under Representative Concentration Pathway 8.5. The practical benefit of an allowance is that it condenses a distribution of MSL projections into a single recommendation. Our basic allowances are founded on the probability distributions of all components of time-mean sea-level rise considered by UKCP18 except the Antarctic dynamic uncertainty. For the Antarctic dynamic contribution we include the mean projection. These basic allowances are suitable for use in situations where there is some tolerance of uncertainty. The allowance is always greater than the central estimate (50th percentile) of mean sea-level rise. Our basic allowance is found to lie below the 80th percentile in most cases, and below the 90th percentile in all cases, throughout the 21st century. We find significant sensitivity of the allowance to the uncertainty (but not the curvature) of the present-day return level plot. We find that the normal approximation is legitimate for the sum of all components of time-mean sea-level rise considered by UKCP18 except the Antarctic dynamic uncertainty. UKCP18 used a lognormal distribution to parameterise the Antarctic dynamic contribution. For a lognormal distribution of MSL rise, Hunter’s allowance is not finite. Modifying the lognormal distribution, we give an additional allowance for the Antarctic dynamic contribution uncertainty, which could be added to the basic allowance in situations where there is less tolerance of uncertainty. This additional allowance is small (less than 1 cm at any UK location before 2060) compared to the basic allowance, and, at least over the 21st century, it is virtually independent of greenhouse gas concentration pathway.
Recent advance of carbon dots in bio-related applications
Authors: Qu Dan , Wang Xiayan , Bao Yuping , Sun Zaicheng
Published: March 2020

Carbon dots (CDs) is a kind of carbon nanoparticles with a plentiful of surface functional groups and tunable emission with different excitation wavelength. Broadly speaking, CDs include carbon nanodots, carbon quantum dots, graphene quantum dots, carbonized polymer dots. Due to the unique nature, they are explored for various applications in the bio-related fields such as bioimaging, sensor for ion and (bio)molecules, catalyst, LED and other fields. They are viewed as great alternative tracers to the current fluorescent biomarkers in personalized nanomedicine and surgery operation monitoring. In this review, we summarized the recent progress in the development of CDs, including improvement in fluorescence properties, two-photon fluorescence, and integration with other modalities as theragnostic agents. Specifically, we discussed the preparation of dual-modal imaging agents to improve the accuracy of diagnosis, the combination of imaging and targeting functionality for the effective accumulation of biomarkers, and the integration of imaging and therapeutic agents to effectively monitor the localization and concentration of therapeutic agents. Finally, the theragnostic agents composed of three functionalities (e.g. targeting, imaging, and therapy) were summarized to provide readers with future perspectives in this field.