bluuebunny/crossref_metadata_2025 huggingface dataset:
The autoregressive integrated vector model approach for covid-19 data in Indonesia and Singapore
Authors: Fitriani R , Revildy W D , Marhamah E , Toharudin T , Ruchjana B N
Published: January 2021

Almost all of the countries in the world were attacked by coronavirus disease 2019 (covid-19) which is started from pneumonia endemic cases in Wuhan China in December 2019. The virus spreads including in the Southeast Asian region where Singapore and Indonesia are two countries with the highest cases. Simultaneous modeling of cases in both countries is important as information on the development of covid-19 cases. The Vector Autoregressive Integrated (VARI) model is a multivariate time series model that can be used to build model non-stationary time series data in several locations simultaneously. This study aims to analyze the development of covid-19 cases and builds a VARI model of covid-19 cases in Indonesia and Singapore. The data used from daily covid-19 confirmed cases in the period March 16th until April 19th, 2020. Plotting and statistics testing covid-19 data series from both countries show non-stationary series which have trend and fluctuation. Based on optimum lag identification use the minimum Akaike Information Criteria correction (AICc), the parsimony model is obtained namely VARI(1,1) which has satisfied the multivariate normal, white noise, and homogenous assumption. The result shows that covid-19 cases in Indonesia and Singapore have a strong positive correlation. However, the covid-19 cases in both countries were only influenced by previous cases in each country. The accuracy shows that the model is good enough for forecasting covid-19 cases in both countries.
Modeling of covid-19 in Indonesia using vector autoregressive integrated moving average
Authors: Meimela A , Lestari S S S , Mahdy I F , Toharudin T , Ruchjana B N
Published: January 2021

A phenomenon of coronavirus became a big deal around the world at the end of December 2019. To find out how deadly the disease is, we can use the Case Fatality Rate (CFR), which provides the ratio number of deaths due to covid-19 between founded cases number of covid-19. However, studies to see the relationship between the number of cases and the number of deaths caused by covid-19 in Indonesia rarely done. Time Series analysis that can see how the relationship between the number of cases and the number of deaths due to covid-19 in Indonesia is Vector Autoregressive Integrated Moving Average analysis (VARIMA). Data used in this model must be qualified the stationary. For that reason, the transformation using differencing and logarithm on data must be performed to resolve non-stationary. The result shows the model that fulfilled all assumptions and had the smallest AICC value is VARIMA (1,1,1). The model shows the number of cases influenced by the number of cases and the number of deaths in the previous period. The same condition applies to the number of deaths affected by the number of deaths and the number of cases from the preceding period.
Geographically weighted models for modelling the prevalence of tuberculosis in Java
Authors: Noorcintanami S , Widyaningsih Y , Abdullah S
Published: January 2021

Indonesia’s position as one of the high burden countries for the infectious disease, tuberculosis (TB), has caused TB to be a major health problem in Indonesia. As means to control the number of TB cases, it becomes important for the government to identify factors associated with it. Commonly, multiple linear regression models are used to evaluate the linear relationship between the identified factors and the number of TB cases. Unfortunately, this model does not have the ability to expose the spatial variation in the data. Therefore, this study proposes to implement a spatial model: a model that takes the geographical location in the model. This research examined two types of geographically weighted models (GWM): geographically weighted regression (GWR) and mixed geographically weighted regression (MGWR). These spatial models assign weights to observations based on its’ geographical location. These two models were constructed to evaluate the relationship between the prevalence of TB in regency/city in Java in 2017 and the factors associated with it: population size, success rate of TB treatment, percentage of toddlers receiving BCG vaccine, percentage of HIV patient, percentage of household with adequate sanitation, percentage of poor people and the number of public health centre per one hundred thousand people. Akaike’s Information Criterion (AIC) and adjusted R2 were used to assess the model performances. We found that the GWR model fits the data better than MGWR, as it has a smaller AIC value (1558.67) and a higher adjusted R2 (0.754). It is also found that BCG vaccine is important to reduce the prevalence of TB, as the percentage of toddlers receiving BCG vaccine is negatively associated with it. Among the examined areas, Jakarta is the area with the highest association between the percentage of toddlers receiving BCG vaccine and the prevalence of TB.
Empirical evaluation on discounted Thompson sampling for multi-armed bandit problem with piecewise-stationary Bernoulli arms
Authors: Asyuraa F C , Abdullah S , Sutanto T E
Published: January 2021

The Multi-Armed Bandit problem is a problem in reinforcement learning that focuses on how to solve the exploration-exploitation dilemma. The dilemma is, given a set of options (known as arms) that you could try many times, how to balance between gathering information through experimenting with the available arms (exploration) or maximizing profit by choosing the seemingly best arm at that time (exploitation). The Multi-Armed Bandit problem is centered around determining which arm to choose at every round. Multi-Armed Bandit has gained its popularity as a more dynamic approach to a randomized trial, with its goal is to experiment with each available arm while still maximizing profit gained. An example of Multi-Armed Bandit in real life is in determining which film artwork should be shown to a visitor that would attract the visitor to watch that particular film. Bernoulli distribution with parameter θ is chosen to model the response of the visitor after seeing the artwork. Non-stationary condition on θ can be implemented to accommodate various trends in film artworks. Some artworks might be good at a certain month, but they could not be preferred in the next month. The non-stationary condition in this study is modeled through piecewise-stationary. We implemented a discounted Thompson sampling policy that used Bayesian method to determine which arm to choose at each round. Multiple simulations were conducted on various conditions to empirically test the policy’s performance on various conditions. Evaluation was based on the cumulative regret. Based on these simulations, discounted Thompson sampling policy achieved relatively lower cumulative regret in tackling the stationary and piecewise-stationary conditions, compared to some well-known policies such as Epsilon Greedy, SoftMax, Upper Confidence Bound, and Thompson Sampling.
Bayesian MSTBurr mixture model in the construction of 3D-MRI brain tumor images
Authors: Pravitasari A A , Iriawan N , Fithriasari K , Purnami S W , Irhamah  , Ferriastuti W
Published: January 2021

Detection of a brain tumor could be done with the serial of MRI images. The location and size of the tumor should be determined by viewing the 2D images individually. This kind of analysis is inefficient and error-prone. For better visualization, this study reconstructs a 3D structure from 2D MRI images. In recognizing the brain tumors, image segmentation is performed using the clustering analysis via Bayesian MSTBurr Mixture Model. The optimum cluster is selected by calculating the Correct Classification Ratio. The segmentation results for each image slice are performed in 3D rendering with the Matlab Volume Viewer. This study succeeded in creating a 3D model with a segmentation accuracy of 93.66% and an estimation of the tumor volume of about 33,556 mm3.
Designing PISA-like task on uncertainty and data using Covid-19 context
Authors: Zulkardi  , Nusantara D S , Putri R I I
Published: January 2021

COVID-19 pandemic is an example of a global context and real-world phenomena. This context is an excellent example of the uncertainty and data, a part of the content knowledge in the PISA 2021 framework. This new content of the uncertainty and data as a part of mathematics literacy focuses on conditional decision-making. This paper aims at sharing the development and implementation of the PISA like task using three steps of design research method. Firstly, a preliminary study on the history and development of PISA in Indonesia. Then, the PISA-like items’ design process continued with the tasks’ experiment using focus group discussion. In the end, the PISA problems have resulted after a formative evaluation. Finally, it is produced valid and practical of three PISA-items and had a potential effect on students’ mathematical literacy.
The implementation of the discovery model based on numbered head together reviewed from students’ cognitive style in mathematics learning
Authors: Setyaningsih N , Utami D P
Published: January 2021

Students consider that mathematics learning delivered by the teacher to be very boring, teacher-centered learning and most students said that mathematics difficult to understand. For this reason, this study aims to analyze the implementation of the discovery model based on NHT in mathematics learning in terms of students’ cognitive styles. This study uses a quantitative approach with a quasi experimental research design. The population in this study were all 7th grade students of SMP Karangmalang Sragen. The sampling technique in this study was using cluster random sampling technique. Meanwhile, the data analysis technique used two-way analysis of variance. The results showed that there were differences in the achievement of mathematics learning outcomes in terms of the use of learning models and students’ cognitive styles.
Malodorous Gas Sensor Array System for the Olfactory Robot
Authors: Zhang Xu , Zhang Sixiang , Ma Xu
Published: January 2021

Malodorous gas sensor array system for the olfaction robot is developed with four sensors including two semiconductor metal oxide sensors, a electrochemical sensor and a photoionization detector (PID). The system structure and experiments analysis are introduced in this paper. Result of experiments show that the steady system can keep ±0.03V fluctuation limited from 0 to 5 V after it stabilized and the maximum difference of response less than 0.02V. It means that the developed system can be satisfied for the olfaction application of robots in malodorous environment.
Research on Robot Trajectory Planning Strategy for Water Polo Confrontation
Authors: Wang Chao , Xu Yuefan , Li Hongyang
Published: January 2021

In the multi-joint robot water polo competition, it often occurs that the robot fish glances off the ball when pushing it due to the water waves generated by the fish swimming and the influence of the opponent fish’s attack behavior on the target ball. In order to improve the success rate of the robot fish hitting the ball, this paper proposes a long-distance posture adjustment strategy and double-fish alternative attack strategy. The long-distance posture adjustment strategy is based on the detailed division of the relative positions of the fish, the ball and the goal. The probability of the fish, the ball and the goal being in a three-point line is improved by setting the distance through which the fish body reaches the preset hitting point and the posture adjustment strategy. Besides, the double-fish alternative attack strategy is proposed on the basis of this ball-pushing strategy. The cooperation between the two fishes is strengthened by developing another fish’s returning strategy during the opponent fish hitting the ball, thereby improving the attack efficiency. The experimental results show that the underwater robot fish can avoid the close touch-ball and can attack the ball into the goal more quickly and accurately.
A stable feature selection method based on relevancy and redundancy
Authors: Shen Peng , Ding Xiaoming , Ren Wenjun , Liu Shu
Published: January 2021

In this paper, the characteristics of software defect prediction are analyzed from the perspective of machine learning. To solve the problem of some redundant or uncorrelated features in defect data sets, a stable feature selection method based on relevancy and redundancy (RRSFS) is proposed. RRSFS combines the redundancy between features and the correlation between features and classes to select the optimal subset. RRSFS not only reduces the cost of data operation in the prediction model, but also enhances the stability of feature selection algorithm.
Research on Chinese Event Extraction Method Based on HMM and Multi-stage Method
Authors: Zhan Liying , Jiang Xuping , Liu Qian
Published: January 2021

The existing Chinese event extraction methods are mostly one-stage methods. Even if there are two-stage methods, the correlation between the stages is not high, the extracted event trigger words and event elements have low matching degree, and there is a class imbalance problem in the training process. This paper proposes a Chinese event extraction method based on HMM and multi-stage method. The first stage of the method recognises the event trigger word positive examples in the text; the second stage classifies the trigger word positive examples recognised in the first stage, determines the event type, and forms the sequence of event trigger words; the third stage matches the event elements, and forms the sequence of event elements, according to the first two stages of event trigger word extraction results. This method effectively alleviates the class imbalance problem in training, improves the matching degree between event trigger words and event elements extraction, and obtains better extraction performance. The accuracy, recall rate and F value of Chinese event extraction are all More than 78%.
Electric Power Equipment Image Recognition Based on Deep Forest Learning Model with Few Samples
Authors: Yao Nan , Cheng Ke
Published: January 2021

Power system equipment have many characteristics, such as many kinds, single color, similar appearance and complex environment of transmission and substation. These characteristics make it difficult for traditional image recognition technology to achieve good recognition results. If there are few training data samples, the performance of in-depth learning recognition declines sharply. To solve these problems, this paper designs an optimization and training recognition method based on the combination of generating confrontation network and deep forest. Firstly, the generated countermeasure network is used to expand the samples of power equipment, and then the active learning method is used to optimize the generated samples. Secondly, the traditional model-enhanced sample expansion method is used to expand the optimized samples twice. Finally, the robust network model is obtained by using the deep forest method to accurately identify the power equipment. Experiments show that this method has high recognition accuracy and is superior to many other algorithms.
Cricket Video Events Recognition using HOG, LBP and Multi-class SVM
Authors: Abbas Qamber , Li Youmeng
Published: January 2021

The world has witnessed a growth in multimedia data, especially video data over the past few years due to increased internet bandwidth and higher processing power of computers. Having a large number of video data also require techniques to store, summarize, index and information retrieval. More attention has been given in recent years to develop techniques which summarize, index and retrieve sports videos due to its commercial aspects. This paper proposes a framework which classifies a cricket video into one of the four events namely Bowled Out, Caught Behind, Catch Out and LBW Out. The framework uses training videos from each category of event and summarizes the videos into key frames. HOG and LBP features are computed for key frames and fused to form a single feature vector which will be labeled accordingly which represents a single event in video. Feature vector is given to a Multi-Class SVM which classifies the video into one of the four events. The experimental results show that the Precision of our technique is 77.23%, Recall is 77.86%, F-Measure is 77.55% and the Accuracy is 65.62%. The evaluation metrics of our technique are promising, because in the literature there is no other technique present so far for event detection & classification in cricket videos.
Malicious Traffic Classifier in android using Neural Networks
Authors: Abbas Qamar , Abbas Qamber , Jin Jesse S.
Published: January 2021

Now a day’s android is the fastest growing package in hand-held operating system. And it has become the most appealing and practical goal of malicious applications. This principal platform has approved itself not only in the mobile global but also in the Internet of the Things (IOT) devices. The most real risk of Android clients is malware contamination by means of Android application markets. The huge diffusion of malware in cellular platform is plaguing customers. Threat of malicious software has come to be an essential element within the protection of smartphones. The war between protection analyst and malware intellectual is abiding as contraption grows. The proposed methodology demonstrated the common precision 94% a tagged mobile malware datasets visitors with a lot of applications contains benign and twelve very different groups of all malware and adware in particular and complicated nature of malware is converting quickly and therefore emerge as harder to understand. We assessed different blends of oddity location calculations, include determination strategy and the quantity of the top highlights to decide the mix that gives the best activity in distinguishing new malware on Android. This research condenses the malware progression recognition procedures backed machine learning algorithms devoted to the Android OS.
Automatically Trimming Profile Based On Triangular Mesh Model
Authors: Kong Jihong , Wu Chao , Qiu Enhua , He Man , Lv Yisong
Published: January 2021

The article introduces an automatic algorithm for cutting contour based on triangular mesh model, which realizes the solid section, automatic filling and material inheritance of the 3D model in the virtual reality environment, which can be accurately visualized in the virtual reality environment. It can not only describe the solid section of the 3D model, but also inherit the material texture relatively accurately, and it has a good real-time. The algorithm is applied to the immersive virtual reality simulation platform of the hydropower plant, which can allow students to perform real-time 360-degree viewing and cutting of the equipment of the power plant, and improve the knowledge level of the employees of the power plant on the internal structure of the equipment.
Research and Application of Multipath Network Time Service System
Authors: Xu Jie , Wu Jing
Published: January 2021

The time source of this paper is based on Shanghai Laboratory of National Time-Frequency Measurement Center. The target of this paper is to build a multipath network timing system within the wide area network which could make sure the accuracy, reliability and safety of network time service. This Multipath Network Time Service System also playing an influential role in providing dedicated network time services for seconds-level time-demanding industries. The pharmacological analysis of the medical industry is used as a service demonstration case to verify and promote the application of the multi-path network timing system.
Image Inpainting Using Double Discriminator Generative Adversarial Networks
Authors: Zhang Chao , Wang Tong
Published: January 2021

The image inpainting algorithms based on GAN have problems such as color inconsistencies of the inpainted region with the surrounding part and the mode collapse during the training. This paper proposes an inpainting algorithm based on double discriminator generative adversarial networks (GANs). This algorithm introduces the loss function of WGAN-GP into a global discriminator and local discriminator independently. It combines the Mean-Square Error (MSE) and feature reconstruction loss function to train the inpainting model and repair the missing area. The algorithm uses more dilated convolutions instead of standard convolutions to obtain a larger receptive field and the skip connection to enhance the structural prediction ability of the generator. The experiment results on the labeled faces in the wild dataset show that our algorithm improves the quality of image inpainting and the stability of the training process.
Research on intelligent operation and maintenance management method of enterprise IT
Authors: Wang Xiaoshuang , Su Yaofeng , Li Qiang , Han Feng
Published: January 2021

With the gradual deepening of the influence of artificial intelligence technology on all walks of life, enterprise IT operation and maintenance management is also undergoing changes in a more intelligent and advanced direction. In order to change the practical problems of enterprise IT operation and maintenance management, such as single method and lack of means, this paper analyzes and studies the overall framework of enterprise it intelligent operation and maintenance management technology, and deeply analyzes the key intelligent methods in operation and maintenance management, such as fault prediction, fault analysis, operation and maintenance scheduling, so as to provide support for effectively improving the level of enterprise it intelligent operation and maintenance and systematic support ability.
Design of Surface Roughness Evaluation System Based on UWP
Authors: Sun Kaixin , Liu Na , Cai Xiaoyu
Published: January 2021

The surface roughness algorithm and its evaluation system is the key factor of surface morphology measurement. This paper studies the UWP (Universal Windows Platform) platform and uses C# language to realize surface roughness evaluation system. The system can accurately calculate 4 commonly used surface roughness parameters based on the established mathematical model. The system includes 3D surface data reading, surface roughness calculation, graphic interaction and other functions. Experiments results show that the system can realize rapid and multi-parameter measurement of the workpiece’s surface roughness, and is adequate for surface morphology characterization. The cross-platform evaluation system is user-friendly with visualized 3D data and reliable results.
Improved Particle Swarm Optimization Algorithm Based on Dynamic Change Speed Attenuation Factor and Inertia Weight Factor
Authors: Wang Chao , Ou Yang , Shan Zhiyong
Published: January 2021

Compared with other optimization algorithms, the particle swarm optimization algorithm (pso) has the advantages of fast convergence, simple calculation, and fewer parameters to be adjusted. Therefore, it has been greatly developed in the past period of time. In order to further accelerate convergence, in recent years, more efficient and convenient particle swarm optimization algorithms such as LDWPSO, NLPSO, and DPSO have been proposed.However, these algorithms have many problems such as large amount of calculation, lack of precision, and easy to fall into local optimum.Therefore, the algorithm still needs to be improved. In order to obtain an efficient global search ability early in the algorithm iteration and better local optimization performance in the later stage of the algorithm iteration, this paper proposes a special expression form of dynamic change speed attenuation factor and inertia weight factor, and in the algorithm some random parameters are added to the iterative formula and applied to the algorithm iteration formula to improve the optimization speed and optimization effect of the algorithm.Finally, the effectiveness and correctness of the algorithm are also demonstrated by computer simulation experiments.
A Novel Image Encryption Algorithm Based on Bernoulli Shift Map
Authors: Guo Youmin , Zheng Dateng , Liu Dingning , Li Fupeng
Published: January 2021

This paper puts forward a chaotic encryption algorithm based on Bernoulli Shift Map. This algorithm adopts dynamic encryption key and uses plaintext image parameters to control generation of chaotic key, and enhance connection between chaotic key and plaintext data; the iterative operation precision of chaotic map during encryption is high, avoiding cycle of chaotic system iteration process. The NPCR and UACI analysis, adjacent pixels correlation analysis and image information entropy analysis on encrypted image show this encryption algorithm has favorable encryption characteristics such as larger space for key, sensitivity to plaintext and random distribution of ciphertext.
Research on the application of Decision Tree and Random Forest Algorithm in the main transformer fault evaluation
Authors: Zhang Chenmeng , Hu Can , Xie Shijun , Cao Shuping
Published: January 2021

The detection and monitoring methods of power equipment are relatively complex. The analysis of a large amount of data obtained from detection and monitoring and the development of data recording methods make it possible to obtain higher-dimensional power data. If we adopt appropriate data analysis method to the data sets, we can better get the potential laws and value of power data. In this paper, we took various basic monitoring data and faults records of the 110kV main transformers into consideration, and the grid-equipment-environment data was fused. Based on the fused data, we used the decision tree and random forest algorithm to evaluate the main transformers’ defects and faults. The evaluation results of the two algorithms were obtained and compared, which proved the effectiveness of the fault evaluation algorithm and selected a more accurate fault evaluation algorithm. This paper provides new ideas for smart fault detection for power grid, and provides a reference for a more in-depth evaluation of power grid equipment.
Analysis and Risk Assessment of Heavy Metal Residues in Fish in Songhua River Basin
Authors: Chen Xin , Liu Yun , Jia Liming , Zhao Yue , Zhao Ran , Dong Yingli
Published: January 2021

Thirty samples of crucian carp and carp are collected in upstream pine forest, midstream Dadingzi mountain and downstream Jiamusi section in the main stream of Songhua river. The ICP method is used to test heavy metals content of the fish. The exposure health risks of exposed way of edible fish in the Songhua River is assessed by exposure to the health risk assessment model. The results show that the noncarcinogenic target risk quotient (THQ) of each heavy metals is less than 1. The biggest heavy metal of the noncarcinogenic risk is As. From high to low in the order, the THQ is As>Cu>Mn>Pb>Cd. Composite exposure non carcinogenic hazard index (HIsF) is 0.18∼0.23. As of fish exposed can cause the evident in the risk of cancer, TR is 2.89×10−5∼6.78×10−5.
Yuetian soil conditioner can enhance the output, decrease the concentration of heavy metals of areca taro and reduce soil acidity
Authors: Huang Dongfeng , Luo Tao
Published: January 2021

The influences of using several dosages of Yuetian soil conditioner on the output, heavy metal concentration of areca taro and soil basic fertility characteristics were studied by field plot experiment. Outcomes indicated that the output of areca taro enhanced by 6.2%∼12.8%, As concentration decreased by 28.9%∼40.0%, Hg concentration decreased by 28.6%∼50.0%, soil pH value enhanced by 4.0%∼34.9%, exchangeable acid decreased by 10.5%∼95.2%, available phosphorus decreased by 36.4%∼54.6%, and the concentration of organic matter, hydrolytic nitrogen and available K was different, but the concentration of Pb, Cd and Cr enhanced, and the concentration of Hg and As was different.
Rainfall forecast of Tongjiqiao reservoir
Authors: Sun Leilei , Guo Huifang
Published: January 2021

The dam site of Tongjiqiao reservoir is located in Puyang River, a tributary of Qiantang River. Tongjiqiao reservoir is the backbone flood control water conservancy project in Puyang River Basin and an important source of ecological and environmental protection tourism. It is also a reserve water source protection area for the future development of Pujiang County. The reservoir not only provides most of the industrial and agricultural production and domestic water for several major towns (streets) and economic development zones of Pujiang County, but also undertakes the ecological water supply task of Puyang river. For a long time, Tongjiqiao reservoir has played an important role in the economic and social development of Pujiang County. In this paper, the model of adaptive neural fuzzy inference system is used to predict the rainfall of Tongjiqiao reservoir. The results show that the rainfall of the reservoir area is in the average level of many years from 2020 to 2025.
Research on the Integrated Support of Equipment under Green Ecological Concept
Authors: Yang Lili , Xuan Zhaolong , Liu Tielin , Zhang Xiaoliang , Huang Xinxin
Published: January 2021

In the context of the country’s great efforts to build a socialist ecological development, the weaponry and equipment, as an important part of the development of the national defense, are becoming more and more important in the direction of green ecological environment. The article analysed and discussed several key ways of implementing green integrated support of equipment-----green design for equipment, green manufacturing for equipment, and green maintenance for equipment, which promotes coordinated and continuous development of military, environment and economy on the basis of expounding the connotation of green ecological environment and equipment integrated support.
Research on conflict handling mechanism of EPC project based on partnering mode
Authors: Cheng Fan , Yin Yilin
Published: January 2021

The concept of win-win cooperation emphasized by partnering mode can promote all parties of the project to achieve win-win, and can effectively reduce the conflict and controversy caused by the confusion of work interface, poor communication and coordination and other issues in the project. In this study, first of all, rough set method in conflict analysis theory is used to reflect the conflict degree of each participant on a certain problem and the main conflicts in the problem. Then, combined with the conflict level division and conflict ejection mechanism in partnering mode, a conflict handling mechanism of EPC project based on partnering mode is constructed, which provides a theoretical basis for conflict handling of EPC project According to and practical guidance.
Application of a novel phosphonic-functionalized polyether imidazolium ionic liquid in hydroformylation of 1-octene
Authors: Yao Jiajun , Ma Qingqing , Jin Xin
Published: January 2021

In this paper, based on the concept of integration of phosphine ligands and ionic liquids, a new phosphine-functionalized polyether imidazolium ionic liquid [Ph(EO)16MIM]3[P(m-C6H4SO3)3] was designed and synthesized. The phosphine-functionalized ionic liquids have the characteristics of both phosphine ligands and ionic liquids. They can be used not only as ligands to complex metal rhodium, but also as catalyst supports. Based on this unique property, we constructed a Rh-[Ph(EO)16MIM]3[P(m-C6H4SO3)3] biphasic catalytic system to catalyze the hydroformylation of 1-octene. The results show that the catalyst is not only easy to separate and recover, but also can obtain 2717 h−1 TOF value within 0.5 h. When the catalyst was reused for 15 times, there was no obvious deactivation of the catalyst, and the total conversion number TTON was as high as 43288.
Influence of Yuetian soil conditioner on output, heavy metal concentration and soil improvement of rice
Authors: Huang Dongfeng , Luo Tao
Published: January 2021

A field experiment was conducted to test the influences of several doses of Yuetian brand soil conditioner on dry matter output and heavy metal concentration of rice, and the influence of basic physical and chemical properties of soil. The outcomes indicated that the dry matter output of rice was increased by 1.29%∼23.42% by using different dosage of Yuetian brand soil conditioner compared with no soil conditioner treatment (treatment 1). The effect of Treatment 5 (1875kg/ha) was better on enhancing dry matter output of rice. And they could reduce Cd and Pb concentration of rice by 17.32%∼60.17% and 11.47%∼64.68%, respectively. Ttreatment 5 and 6 had a better effect on reducing heavy metal concentration in rice, and could increase the pH of the soil by 4.7%∼37.2% and decrease the exchangeable acid (latent acid) by 3.5%∼55.9% , the concentrations of soil effective Cd and Pb were reduced by 9.28%∼49.48% and 8.34%∼24.40% , respectively.
Enhancement characteristics of laser ultrasonic basin-type insulator based on carbon nanocomposites
Authors: Lu Shan , Jia Dafei , Wu Haifei , Fan Jingjing , Xia Hui
Published: January 2021

Laser ultrasonic technology has been widely used in the field of NDT due to its advantages of non-contact and high resolution. The sensitivity of laser ultrasonic detection signal is not only related to laser excitation parameters, but also to the thermal absorption of the target. Therefore, once the measured target is determined, the sensitivity of laser ultrasonic detection based on thermoelastic effect is limited by the damage threshold of the measured target. In order to improve the sensitivity of the detection signal under the damage threshold, it is a good choice to add a layer of enhanced medium with high photoacoustic conversion efficiency on the surface of the laser target material. Based on this, a method based on CB(carbon black)-PDMS composite film was proposed to enhance the sensitivity of detection signal.
Experimental Study of Micro-electrolytic Technology on Ship Sewage Treatment
Authors: Tian Yihe , Guan Di , Wang Weijie , Zheng Jixin
Published: January 2021

Aims to remove oil and anionic surfactants in domestic wastewater on the ship, the micro-electrolysis treatment with fillers was applied in this paper. The effect of the system on the removal efficiency of oil and surfactants was studied, and the optimal operating conditions were determined. The results show that the removal rate of oil and LAS is 81.2% and 77.5% with safe effluent quality under the optimal working conditions.
Analysis and Research of uncertain radius in constructing geo - spatial buffer
Authors: Xu Ya-Nan , Qin Jianxin , Zhou Yunqi , Wu Tao
Published: January 2021

Buffer analysis is one of the most important functions in GIS spatial analysis, which is used to establish the scope of influence around spatial entities. The conventional buffer radius is fixed. However, in practical situations, the buffer radius should be dynamically adjusted by considering the specific environment of the space entity such as nature and socio-economy condition. Therefore it is able to achieve accurate and scientific spatial analysis, which aims to provide efficient decision-making services and intellectual support for the construction of the social economy and the ecological civilization. In this paper, based on analysis of the shortcomings of conventional buffer, several special buffers are discussed and the uncertain buffer radius is calculated under different conditions. Finally, an example is harnessed to illustrate the existence of uncertain buffer radius.
Passive control of a class of fuzzy time-delay systems
Authors: Wang Yanhua , Wang Zhanli , Zheng Liwen
Published: January 2021

In this paper, it is studied the passive control of a class of fuzzy systems with state uncertainty and time-delay. First, a class of nonlinear systems is described by using T-S fuzzy model with uncertain items and time-delay ; Secondly, by using the linear matrix inequality technique(LMI) and designing the state feedback controller, we can obtain the feedback gain matrix , the robust stability of the closed-loop system is achieved by using Lyapunov’s stability theory, at the same time, it is realized the sufficient conditions for passive control of the system. Finally, the feasibility and validity of the results are verified by simulation examples.
Notch correction strategy of LCL-type grid-connected inverters based on GCFAD
Authors: Zhang Junqi , Feng Tingting , Tang Libo
Published: January 2021

Considering the control delay, grid-current-feedback-active-damping(GCFAD) method can ensure the stability of the system through the reasonable design of parameters, but when the grid impedance changes, the system may be unstable because the LCL resonance peak cannot be effectively suppressed. In this paper, a notch correction strategy is proposed. The amplitude drop characteristic of the notch filter is used to effectively suppress the LCL resonance peak in this paper, so as to ensure that the system has sufficient amplitude margin when the grid impedance changes. The robustness of the system is greatly improved to the grid impedance when the GCFAD method is adopted. Finally, the simulation model verifies the effectiveness of the proposed strategy.
Field analysis of switching transient process on the 35 kV side of a substation connecting to PV plant
Authors: Wang Ge , Zhou Yi , Zhu Jingfeng , Chen Shaoqing , Su Tao , Zhao Jingwei , Liang Wei
Published: January 2021

To reveal the real causes of two explosion faults of potential transformer (PT) on a substation connecting to PV plant, an on-line monitoring equipment with a sampling rate of 40 MS/s for the transient voltage was installed on the secondary side of the PT. By switching the 35kV bus bar onto the main transformer, the switching transient processes on the PT were obtained. The field measured transient process indicated that the switching transient voltage caused by switch operation induced the resonance between the charging circuit and the PT and the resonance may be the direct cause of the PT explosion. The effective measures to suppress resonance in this substation are proposed.
Research on master-slave filtering of Celestial Navigation System / Inertial Navigation System
Authors: Yan Fei , Zhao Wangyang , Wang Xingling , Wang Kai , Wang Yuanming
Published: January 2021

As the equipment with the highest attitude measurement accuracy and minimum drift, Celestial Navigation System (CNS) has widely applied and developed on various platforms, such as air-based, sea-based and land-based platforms. Aiming at the application characteristic of the CNS equipment, this article mainly analyzes the two operating modes based on gyro correction and overall estimation of INS error in the shipborne CNS/INS. Based on different selection of measurement information in CNS/INS, optimized choice is made in the integrated mode of gyro drift correction based on starlight and the integrated mode of satellite inertial navigation based on the overall estimation of inertial navigation error. This paper designs a master-slave filtering mode of CNS/INS to switch the filtering according to the observation conditions and verify through simulation analysis. This method can improve the adaptability of CNS/INS to the onboard environment; improve the positioning accuracy of about 40% compared with the single working mode during the long working of the integrated system, which can guarantee the navigation accuracy of the combined system.
Ghosts of landuse past: legacy effects of milldams for riparian nitrogen (N) processing and water quality functions
Authors: Inamdar Shreeram , Peipoch Marc , Gold Arthur J , Lewis Evan , Hripto Johanna , Sherman Melissa , Addy Kelly , Merritts Dorothy , Kan Jinjun , Groffman Peter M , Walter Robert , Trammell Tara L E
Published: January 2021

Milldams and their legacies have significantly influenced fluvial processes and geomorphology. However, less is known about their effects on riparian zone hydrology, biogeochemistry, and water quality. Here, we discuss the potential effects of existing and breached milldams on riparian nitrogen (N) processing through multiple competing hypotheses and observations from complementary studies. Competing hypotheses characterize riparian zone processes that remove (sink) or release (source) N. Elevated groundwater levels and reducing soil conditions upstream of milldams suggest that riparian zones above dams could be hotspots for N removal via denitrification and plant N uptake. On the other hand, dam removals and subsequent drops in stream and riparian groundwater levels result in drained, oxic soils which could increase soil nitrification and decrease riparian plant uptake due to groundwater bypassing the root zone. Whether dam removals would result in a net increase or decrease of N in riparian groundwaters is unknown and needs to be investigated. While nitrification, denitrification, and plant N uptake have typically received the most attention in riparian studies, other N cycle processes such as dissimilatory nitrate reduction to ammonium (DNRA) need to be considered. We also propose a novel concept of riparian discontinuum, which highlights the hydrologic and biogeochemical discontinuities introduced in riparian zones by anthropogenic structures such as milldams. Understanding and quantifying how milldams and similar structures influence the net source or sink behavior of riparian zones is urgently needed for guiding watershed management practices and for informed decision making with regard to dam removals.
Terrestrial Laser Scanner Self-Calibration: Quantitative Evaluation of Minimum Network Configuration
Authors: Salleh S M , Azmi M A A M , Abbas M A , Mustafar M A , Hashim N M , Sulaiman S A , Salleh C S
Published: January 2021

Two approaches available for performing self-calibration are: 1) point-based and 2) feature-based. The reliance on planes in the feature-based approach is a restriction to the aim of finding a suitable approach for on-site calibration. Also, the requirement for a strong network configuration in point-based self-calibration has made it difficult to implement on-site. The aim of this study was to investigate the suitability of lowering the network configuration requirements. Investigations of minimum network configurations were carried out by analysing minimum dimension of real-world plane on which targets are distributed. Through network analysis, standard deviation and plotted error ellipses will be used to determine the reliability of each configuration during minimising network experiment. The outcomes of this study will benefit to TLS practitioner to ensure that the employed instrument reliable to yield quality data.
Cross Section Intervals of Flood Intervals of Flood Inundation Mapping at Ungauged Area
Authors: Hashim Suhaila , Suhana Mokhtar Ernieza , Ikhwan Abdul Halim Ahmad , Mohd Naim Wan Mohd Wan , Aizam Adnan Nor , Pradhan Biswajeet
Published: January 2021

Digital Elevation Models (DEMs) are essential to present the continuous surface elevation and is used for flood mapping. The use of different cross-section intervals obtained from the various spatial resolution of DEMs will affect the flood depth and inundation area estimation. Therefore, a comparison study is carried out to investigate the effect of cross-section intervals on flood expansion and flood depth which is simulated using one dimensional (1D) HEC-RAS model at Padang Terap River, Malaysia. Two digital elevation models (DEMs) imageries, Interferometry Synthetic Aperture Radar (IFSAR) and Shuttle Radar Topography Mission (SRTM) are used in this study. The result was evaluated using likelihood measures (F-statistics, root mean square error (RMSE), and mean absolute error). The findings reveal the IFSAR DEM with cross-section interval 50 m has higher F-statistics of 70% on flood inundation estimation. By proposing the methodology, flood mapping can be provided accurately by considering the error that exist in the Geographical Information System (GIS) spatial context.
Tree inundation period determination for mangroves rehabilitation monitoring
Authors: Dali H M , Mokhtar M A M , Ghazali N , Zainal M Z , Zainuddin K
Published: January 2021

Mangroves services as coastal bioshield have been long acknowledged after the presence of mangroves has diminished a series of destructions from the extreme coastal force as a coastal barrier. However, to date, mangroves have been under threat mostly by anthropology activities. Mangroves deforestation happened at alarming rates and mangroves replanting, and rehabilitation effort yet to reach an acceptable success rate. To improve the success rate, mangroves inundation period monitoring using tacheometry method has been proposed. The approach used datum projected land elevation and mangroves tree height and overlay with annual tidal fluctuation from tide prediction tables. The output is a total inundation period for predetermined mangroves trees. Mangroves area geometry (MAG) was established at Kampung Sungai Melayu, Johor Bahru as a pilot area. Land elevation and mangroves tree height were measured using tacheometry method. Tidal fluctuations were generated based on the year 2020 Johor Bharu tidal prediction tables. From the overlay of these two datasets, five out of 40 mangroves tree in MAG area never been fully inundated but rather partly under the water surface during the high-water tide. A significant finding from the overlay is, two mangroves trees spending more than half of the year 2020 being submerged underwater and still survived. Despite the time consuming, tacheometry method for mangroves tree growth monitoring did provide a reliable and precise tree inundation period measurement.
Evaluation of Satellite Based Rainfall Estimation
Authors: Mohamad Z , Bakar M Z A , Norman M
Published: January 2021

Rainfall is one major source of water supply needed for most human activities encompassing agricultural activities to heavy industries. Therefore, a reliable and up to date approach shall always be required to accurately quantify rainfall precipitation. In Malaysia, there are three basic systems for providing rainfall measurements; which are conventional ground-based rain gauges, weather radar precipitation, and satellite-based precipitation. This study focused on data from Multi-Functional Transport Satellite (MTSAT-1R) and Terminal Doppler Weather Radar product for rainfall estimation in Malaysia. Both data were verified with the ground rain gauge data. Comparative analysis towards the data was taken based on daily and hourly approaches. Daily rainfall data were taken at Bukit Rajah, Batang Kali, Connaught Bridge, Petaling Jaya and Subang Jaya while hourly rainfall datasets were taken at Petaling Jaya and Subang Jaya. The satellite and radar data is processed using MTSAT HRIT and IRIS software respectively with the aid from Malaysian Meteorological Department (MMD). The correlation coefficient is used in this study to investigate the relationship between the satellite and radar with ground rain gauge data.