bluuebunny/crossref_metadata_2025 huggingface dataset:
A Thematic Portfolio and Recommended Study on the Usefulness of Online Medical Reviews Based on QCA Methods
Authors: Wang Yonghong , Huang Lei
Published: February 2021

The online comment for doctors plays an important role in assisting patients to master the real medical situation and choose medical treatment, which greatly reduces the adverse effects of online medical market information asymmetry. but because ordinary patients lack professional medical knowledge and can not accurately and efficiently write comments that are useful to similar patients, we identify useful topics from online medical reviews and make combination recommendations based on patients with different diseases. guide patients to write comments from the most useful dimensions to alleviate the problem of information overload, thus maximizing the limited human resource utility. This study aimed at online medical platforms such as good doctors and micro-doctors collected online doctor reviews for major diseases and used LDA topic mining and fsQCA fuzzy set qualitative comparative analysis to analyze key topics that affect the usefulness of reviews and optimal topic combinations under different disease types.
Research on Automated Detection of Sensitive Information Based on BERT
Authors: Ding Meng , Wang Xing , Wu Changming , Wang Kaixuan , Yang Xue
Published: February 2021

With the booming of the Internet, Web public opinion plays an increasingly important role in the stability of the network community. Therefore, the sensitive information hidden on the Internet is likely to lead to unpredictable social impact. This paper focuses on the detection of Chinese sensitive information. First, we build a corpus to train the detection model. Secondly, we apply the Bert method to the detection problem. Then, many popular NLP methods are applied to this problem to show the progress of Bert in a sensitive information detection task. Finally, we got a sensitive information detection model based on BERT with a high F1 score of 97.31.
Research on Short Text Classification Based on TextCNN
Authors: Zhang Tianyu , You Fucheng
Published: February 2021

The TextCNN model is widely used in text classification tasks. It has become a comparative advantage model due to its small number of parameters, low calculation, and fast training speed. However, training a convolutional neural network requires a large amount of sample data. In many cases, there are not enough data sets as training samples. Therefore, this paper proposes a Chinese short text classification model based on TextCNN, which uses back translation to achieve data augment and compensates for the lack of training data. The experimental data shows that our proposed model has achieved good results.
Multi-view Bearing Fault Diagnosis Method Based on Deep Learning
Authors: Wang Ruihan , Meng Xinyu , Xiong Bangru , Wang Zhengxia
Published: February 2021

In recent years, the use of Time-frequency analysis to generate Time-frequency diagrams of vibration signals, and then the use of deep learning methods to classify them has become one of the mainstream methods for bearing fault diagnosis. However, a single Time-frequency analysis method usually cannot extract a complete vibration signal, so the accuracy of the model will be affected to a certain extent. Therefore, this paper proposes a multi-view bearing fault diagnosis method based on deep learning. The same segment of vibration signal is generated by different Time-frequency analysis methods, and then the pre-trained network is used to train the model. The Flatten layer was replaced by the Global Max Pooling (GMP) layer before the layer. The experimental results show that compared with the traditional feature fusion method, the method in this paper can not only achieve better accuracy, but also has stronger generalization.
Smart-YOLO: A Light-Weight Real-time Object Detection Network
Authors: Zhang Dongyang , Chen Xiaoyan , Ren Yumeng , Xu Nenghua , Zheng Shuangwu
Published: February 2021

In the computer vision, YOLO has an important position in the field of object detection, but due to its speed limitation, it is not suitable for scenes that require extremely strict real-time performance, such as smart cameras or some mobile devices. However, inverted bottleneck layers, which are built upon depth-wise separable convolution, have been the predominant building blocks in state-of-the art object detection models on mobile devices. In this work, we propose a new lightweight algorithm Smart-YOLO based on the YOLO framework which uses inverted bottleneck blocks and deep-wise separable convolution. We also put forward a new loss function to make up for the loss of accuracy caused by the replacement of the backbone network. The results show that compared with YOLOv3, the accuracy of our model is reduced by about 21%, but achieved up to 4.5× speedup, and the model size is only about 1/8 of the original. This shows that our network is smaller, faster, and more suitable for scenarios that require higher speed and efficient
Research on Inertial Space Intercept Game based on Deep Reinforcement Learning
Authors: Wei Wenshu , Wang Bin , Hou Linjian , Long Han
Published: February 2021

Aiming at the problem of the intercept game in the inertial space, this paper builds a model of basic actions of the two sides of the intercept game in the inertial space, explored the applicability of Deep Reinforcement Learning in inertial space game problem-solving. Based on Proximal Policy Optimization (PPO), this inertial space game problem is solved and the optimal solution is obtained by the reward designing with cumulative miss distance and minimum distance respectively, and finally, the effectiveness of the algorithm based on PPO is verified through the game simulation and results from comparison
Pathfinder-Based Simulation and Optimisation of Personnel Evacuation Modelling of a Shopping Mall
Authors: Han Fang , Liu Lijun , Zhang Yuanyuan
Published: February 2021

This study concerns a simulation and optimisation of the personnel evacuation modelling of a shopping mall in Weiyang District, Xi’an. First, investigations were conducted to determine the human flow rate tand occupant distribution during the rush hours to builda queuing theory model. Then, a simulation was implemented with an evacuation simulation software Pathfinder, followed by an analysis to the evacuation status and bottlenecks. On the basis of the evacuation status, proposals were put forward on how to overcome the bottlenecks to achieve the optimal matching among the personnel, emergency staircases and aisles. Finally, the improved scheme was simulated. Compared with time taken by the evavuation before improvement, the time taken was greatly shortened after improvement, proving the feasibility of the scheme. Through the simulation experimentaiton, it was found that ecacuation modelling can greatly facilitate reseaches on the evacuation problems in multistoryed shopping malls.
Research of Media Technology Development with Programmed Thinking in 5G Era
Authors: Li Jianxun , Su Yancong
Published: February 2021

In a society with highly developed information and communication capabilities in the 5G era, the traditional way of thinking has been unable to meet the digitalization requirements of efficient and feasible. Therefore, we propose a digitalization method to solve practical problems, which is called programmed thinking. In this way, we can imagine each problem as a combination of multiple functions, so that these functions can be determined according to the overall rationality of the requirements of the data content exchange, as well as the overall scheme design. Because this method is implemented in the form of modularization through the overall requirement analysis, even if there is an exception, it is also no need to redesign the entire requirement or function, only need to replace and modify the abnormal part of the method function, so as to achieve the effect of rapid response, switch at any time and simple upgrade.
Underdetermined Mixed Matrix Estimation Based on DPCKFCM Algorithm
Authors: Cao Wenrui , Xu Yan , Nan Zhefeng , Wei Yiming
Published: February 2021

Aiming at the problem of fuzzy C-means (FCM) in the estimation of underdetermined mixing matrix, that the estimation accuracy is not high and the robustness is poor, a density peak clustering (DPC) based on density peak clustering (DPC) is proposed. Improved Kernel-based Fuzzy C-means (KFCM). The kernel function is introduced into the FCM algorithm to construct the KFCM algorithm based on the Gaussian kernel function, which can effectively overcome the influence of noise points and isolated points on the clustering results and improve the estimation accuracy of the mixed matrix; the traditional DPC algorithm is improved and merged with the KFCM algorithm, and thresholds are set for the local density and high-density distance to achieve the initial clustering center of the KFCM algorithm and the automatic determination of the number of cluster centers improves the robustness of the algorithm. Experimental results show that the algorithm has greatly improved the estimation accuracy and robustness of the underdetermined mixed matrix.
Electronic Certificate Sharing Scheme with Searchable Attribute-Based Encryption on Blockchain
Authors: Li Xingjian , Tan Minsheng
Published: February 2021

With the promotion of “Internet +” and smart cities, as an important data voucher, electronic certificate brings great convenience to people’s life. However, the phenomenon of “data island” based on the electronic certificate database of each city hinders the sharing of an electronic certificate. In addition, problems such as centralization of data storage, poor security, and tamper-proof modification are common in electronic certificate libraries. To solve these problems, we present a blockchain-based electronic certificate sharing scheme, which uses the InterPlanetary File System (IPFS) and Ethereum smart contract to achieve secure storage and data sharing of electronic certificate by taking advantage of the non-tampering and decentralization of blockchain. At the same time, we achieve fine-grained access control by using attribute-based encryption of ciphertext policy and assigning attribute keys to users without affecting retrieval efficiency. Finally, through the simulation and performance analysis of the Ethereum test network, the analysis results show that our scheme is effective and feasible.
User-based Collaborative Filtering Algorithm Design and Implementation
Authors: Wang Hulong , Shen Zesheng , Jiang Shuzhen , Sun Guang , Zhang Ren-Jie
Published: February 2021

With the rapid development of social humanities and technology, especially the development of the Internet, the current Internet era, the amount of information is increasing rapidly with explosive growth rate, the information overload problem is particularly obvious, on the accurate acquisition of information from the massive amount of information users want, from which the personalized recommendation technology was born. In order to solve the problem of acquiring the information users want, this paper researches and discusses a kind of personalized recommendation algorithm - a user-based collaborative filtering algorithm, analyzing the user behavior, comparing the advantages and disadvantages of other related algorithms, using the UserCF algorithm, and optimizing the sparse matrix to reduce the time and complexity of the operation. The algorithm is implemented by software to generate recommendation results. The results of the experimental data in this paper show that the algorithm is effective in recommending projects to users.
A Method with Pre-trained Word Vectors for Detecting Wordlist-based Malicious Domain Names
Authors: Lin Shaoqing , Zhong Shangping , Cheng Kaizhi
Published: February 2021

In recent years, botnets have used the domain generation algorithm to generate dynamic typified malicious domain names to bypass various detection methods. Given the depth detection model of such domain names, domain names are generally processed by filling and transforming them into a fixed-length one-dimensional vector and then classifying them with poor detection performance. Therefore, this study first divides the domain into a word array and converts it into a word vector using pre-trained word vector models, Embeddings from Language Models. The domain is inputted into the TextCNN model for training classification. From approximately 100,000 data sets, a 94.22% accuracy rate and 6.87% FPR value can be obtained from the training. Compared with previous detection models (i.e., LSTM and CNN), more training and testing are needed, but improvements are made in all indicators.
Performance Limitation of Sequence Alignment on the NUMA-based Architecture
Authors: Chen Shaolong , Luo Zhenzhen , Gui Xiaolin , Yang Wei
Published: February 2021

In recent years NUMA architecture based on multicore system leads a mainstream solution to tackle the rapidly expanding computation in bioinformatics analysis. Sequence alignment is the most computing cost in the workflow of variant analysis in bioinformatics. Although parallel coding in sequence alignment could be few difficulties, approaching a good performance in such a system is complicated. This research explores the architecture bottlenecks we usually neglect in the implementation of bioinformatics computing, NUMA effect for instance. We exploit the scalability of threads in the sequence aligners to illustrate the problem and the significance of NUMA architecture in the multicore system. The preliminary results in the experiments display that sequence aligners cannot take advantage of NUMA-based multi-core architecture. The scalability of threads is deficient, even negative in time-cost at the case of large-scale genome data larger NUMA nodes.
Design of Parallel Algorithm for Kalman Filter on SW26010 Processors
Authors: Yang Aiqiang , Xu Dandan
Published: February 2021

Kalman filter algorithm, an effective data processing algorithm, has been widely used in space monitoring, wireless communications, tracking systems, the financial industry, and so on. On the Sunway TaihuLight platform, we present an improved Kalman filter parallel algorithm which is according to the new architecture of the SW26010 many-core processors (260 cores) and new programming mode (master and slave heterogeneous collaboration mode). Furthermore, we propose a pipelined parallel mode for the KF algorithm based on a seven-level pipeline of the SW26010 processor. The vector optimization strategy and double buffering mechanisms are provided to improve the parallel efficiency of Kalman filter parallel algorithm on SW26010 processors. The vector optimization strategy can improve data concurrency in parallel computing. In addition, the communication time can be hidden by double buffering mechanisms of SW26010 processors. The experimental results show that the performance and scalability of the parallel Kalman filter algorithm based on SW26010 are greatly improved compared with the CPU algorithm for five different data sets, and is also improved compared to the algorithm on GPU.
Design and implementation of regional online ride-hailing scheduling algorithm
Authors: Hong Anding , Liu Xianfeng , Zhang Jianzhong , Luo Zhangming , Zhang Jin , Cai Meiling
Published: February 2021

The network car has solved some problems of traditional taxis from the perspective of safety and convenience, but there are still gaps in the inter-city field. This paper proposes a scheduling method to solve the inter-city travel field from the regional perspective. Firstly, the platform of the system is built from the perspective of software development, and then the traditional scheduling algorithm for dispatching orders in the city is studied. Finally, the intercity scheduling algorithm is mainly designed. This paper fills the gap in the field of inter-city travel between vehicles, and the scheduling algorithm performs well, with good order taking rate and travel efficiency. The establishment of the regional network car platform meets the needs of the public for inter-city travel. The platform itself has practical significance, and the study of scheduling algorithm also provides ideas and reference for the field of vehicle scheduling.
Manufacturing features, structure, and properties of high-purity Mo-Re thin sheets
Authors: Burkhanov G S , Yusupov V S , Roschupkin V V , Karelin F R , Kirillova V M , Karelin R D , Ermishkin V A , Minina N A , Sdobyrev V V , Serebryany V N , Dormidontov N A , Rodionova S K , Dementiev V A , Lachenkov S A
Published: February 2021

Manufacturing features, structure and properties of low-alloyed molybdenum sheets 0.5 mm thick made from the experimental Mo-3 wt.% Re single crystal are described and discussed in the present work. The developed technological scheme includes warm rolling of the initial ingot at a temperature of 1150 °C, with heating in the argon protective atmosphere, subsequent annealing at 850 °C for 2/3 h in a vacuum, no less than 1 ·103mmHg, cold rolling at a temperature of 400 °C, and post-deformation annealing at 800 C for 1/2 h. The results of micro structure analysis, physical and mechanical studies show that thin sheets are characterized by simultaneously high strength and plasticity, as well as the required temperature coefficient of linear expansion (TCLE), necessary for bonding with a glass by a thermo-compression welding. Obtained results reveal perspectives for the development of the application of thin Mo-3 wt.% Re sheets in vacuum engineering for the vacuum-tight metal-glass parts.
Structure and strength characteristics of rutile ceramics obtained with the approach of oxidative constructing
Authors: Kovalev I , Shevtsov S , Ogarkov A , Shokodko A , Zufman V , Kochanov G , Shokodko E , Strelnikova S , Chernyavskii A , Solntsev K
Published: February 2021

As a result of applying the oxidative constructing approach, compact samples of rutile ceramics were obtained at 875°C for 160 days. An analysis of the distribution of microhardness over the thickness of a rutile sample obtained at 875°C at different times of synthesis indicates an inhomogeneous structure of rutile ceramics, which indicates that the material is defective due to its porosity. The regularities of changes in the nature of the layering of the structure of ceramics are established depending on the portion of the kinetic curve of the direct oxidation of titanium. It is shown that the presence of a defect in the form of a gap between the layers formed during the oxidation of titanium blanks at the stage of completion of the exponential growth of the kinetic curve is due to a natural barrier – a massive rutile ceramic layer, which limits the free access of oxygen to the TiO2/Ti interface.
Chromium adsorption on sodium sulfide treated sheep wool
Authors: Enkhzaya S , Matsune H , Shiomori K , Oyuntsetseg B
Published: February 2021

Chromium adsorption from the aqueous phase was evaluated using Mongolian sheep wool as a biosorbent. The wool was chemically treated with sodium sulfide to enhance the adsorption capacity. The surface of the wool was analyzed by SEM. EDX and FTIR were also used for the analysis of differences in the wool after chromium adsorption. Chromium was detected in the wool after adsorption by EDX analysis. FTIR data indicated the intensity change in amide A and amide I, which shows chromium may be adsorbed to the amino and carboxyl groups of the wool. The sodium sulfide treated wool showed better adsorption capacity of chromium(III) than the untreated wool. In the case of chromium(III), the adsorption amount increased with the pH increased, while for chromium(VI), the adsorption value increased with the pH decreased. Kinetic analysis of the adsorption indicated that the removal of chromium followed pseudo-second order kinetic model for the whole contact time range. Langmuir isotherm model was used for the analysis of adsorption equilibrium and the adsorption followed well Langmuir isotherm. This study demonstrates the sodium sulfide treated wool is an effective and low-cost biosorbent for heavy metal adsorption.
Inhibitory power of young coconut fiber ethanol extract (Cocos nucifera Linn) on the growth of Bacteria staphylococcus aureus and Escherichia coli in tofu
Authors: Sumarni N K , Rahmawati  , Ruslan
Published: February 2021

Analysis of the growth of bacteria Staphylococcus aureus and Escherichia coli has been carried out on tofu that is soaked in ethanol extract of coconut coir (Cocos nucifera Linn). This study aims to determine the lowest total growth of the bacteria Staphylococcus aureus and Escherichia coli in tofu that is immersed in ethanol extract of young coconut fibre-based on variation in concentration a storage time. Young coconut coir is extracted using 96% ethanol and applied in various concentrations from 1000 to 7000 ppm with a soaking time of 24, 48, 72, and 96-hour, respectively. The total bacteria in the tofu from each treatment was calculated by the total plate count method. Based on the results obtained, it can be seen that the lowest total growth of Staphylococcus aureus is 3751 CFU/g, which is immersed at a concentration of 7000 ppm for 24-hour. As for the Escherichia coli bacteria as much as 4737 CFU/g, which was soaked at the same concentration for 96-hour.
Analyzing of 2D resistivity data to determine the subsurface stratigraphy at Institut Teknologi Kalimantan (ITK) Balikpapan
Authors: Rahmania  , Arisalwadi M , Sastrawan F D , Sari D I
Published: February 2021

This research was conducted to identify subsurface stratigraphy at Institut Teknologi Kalimantan (ITK) area. The information of subsurface stratigraphy becomes more important for minimizing the effects of changes in soil structure. We run the Geoelectrical resistivity method using Wenner-array along four measurement lines. Field data measured consists of electrical current and potential difference to calculated apparent resistivity values. Then, the apparent resistivity data were inverted to obtained true resistivity values. The results showed that subsurface stratigraphy was composed of three main layers. The first layer was characterized by higher resistivity values ranging from 39 to more than 86 ohm-meters interpreted as topsoil. The lower range of resistivity values at the second layer was identified as clay with resistivity values from 9.36 to 39 ohm-meters, while the higher resistivity values ranging from 39 to 86 ohm-meters and marked by Brown to red colour below the surface at the third layer was identified as clay sandstone.
Seismic vulnerability analysis in Poso Distric
Authors: Rusydi M , Cyio M B , Rahmawati  , Ramlan
Published: February 2021

High seismic activity in Poso District is not supported by information related to earthquake hazard map with detailed scale. This information is very necessary for supporting the government’s efforts to reduce the risk of earthquake disasters. Therefore, this study aims to map earthquake-prone zones with detailed scale. The study was conducted by measuring Vs30 using a microtremor on each rock sample using the HVSR method, then these values were used as a reference in correcting the USGS Vs30 data, thus producing a VS30 distribution value, with an interval of 1 km. From the results of the study, it showed that based on the Vs30 ratio in Poso District, it obtained a ratio value > 0.5. This value indicates the difference between the results of microtremor measurements and USGS data which was not different significantly. However, some point showed different values to produce a classification of different types of material, thus affecting the distribution of seismic vulnerability. The results of seismic distribution analysis showed that middle lore has a high level of seismic vulnerability from 19 districts spread across Poso Regency
Spatial mapping of natural disaster at Palu Bay
Authors: Rahmawati  , Rusydi Muhammad , Widyastuti
Published: February 2021

Referring to data on natural disasters in three regions, namely Palu City and Donggala Regency, it shows that there were 3 natural disasters that occurred in the last 5 years, namely; landslides, earthquakes, tsunamis. The high level of disaster activity in the three regions is due to the geographical conditions in these two areas which are in the Palu koro fault line, with the morphology of the valley area. Handling of natural disaster management still leaves many problems, one of which is the absence of spatial information related to areas prone to natural disasters at the sub-district level,. The approach used is a spatial approach with an overlay method utilizing geographic information systems (GIS). The results showed that for landslides, the area around the bay is at a low to moderate level of vulnerability, while for the earthquake and tsunami it shows that the southern and eastern parts of the Palu bay are the areas that have the highest level of vulnerability.
Identification of potential liquefaction in Kabonena
Authors: Badaruddin  , Abdullah  , Priambudi M D , Rusli M , Labania H M D , Rugayya S , Asrafil
Published: February 2021

The research was conducted in Kabonena village, which aims to see the subsurface structure and the liquefaction potential. The research was conducted using the geoelectric resistivity method with the Wenner system. The parameters used are geological data, hydrogeology and formation values to obtain between the resistivity value and the lithology of the study area. Furthermore, by considering the geological, hydrogeological conditions and formation factor values, the specific resistance values for each layer are interpreted. The results obtained show that the layers with specific resistance values of 39.16 - 97.9 Ωm are dominated by water-saturated sand/gravel. The resistivity value above 97.9 - 200 Ωm is the layer of molasses and alluvium deposits/coastal deposits, and the resistance value above 200 Ωm is the layer of crushed granite and granodiorite. The existence of a subsurface structure like this, so that in Kelurahan Kabonena it is called a liquefaction event. The location has a layer of saturated air that points to the east with a depth of less than 24 meters.
Analysis of the microcapsule structure based on machine learning algorithm
Authors: Batchuluun Sukhbaatar , Matsune Hideki , Shiomori Koichiro , Bayanjargal Ochirkhuyag , Baasankhuu Tserenkhand
Published: February 2021

For the microcapsules preparation process, the mechanism is estimated by structure reformation during the preparation process since diameter and wall thickness drastically changed. Microstructures are recently studied by machine learning techniques. The Hough transformation algorithm is used by other researchers for the preparation of the microcapsules but it is difficult to determine the mechanism by using only a diameter change of the microcapsules. Therefore, one additional way to establish the mechanism is the analysis of the formation of the microcapsule structure. In this study, The Hough transformation algorithm was used for the image segmentation, the simple feature extractions were checked and the support vector machine and the k-nearest neighbors algorithm were used as classifiers in order to analyze the structure of the microcapsules which were prepared by solvent evaporation method from a solid in oil in water, S/O/W, emulsion system. The structural distribution was analyzed by the developed detection method. The microcapsules had a specific structural distribution which are monocore, multicore, and other aggregated structures. The structural distribution was changed by the preparation condition. The monocore structure was dominant by increasing in the amount of water soluble solid particles added in the organic phase.
Predicting and forecasting of time series models using cluster analysis
Authors: Setiawan I , Sumertajaya I M , Afendi F M
Published: February 2021

Time series model on multiple objects could be univariate and multivariate model. The more objects used in multivariate model would decrease precision of forecast for each object. One way to overcome this problem used univariate models for each object. However, univariate models for each object became inefficient in time. Therefore, clustering performed on objects so that model became efficient. The objective of this research is to study results of predicting and forecasting model with and without clustering. Model and forecasting used time series regression model on broad proportion of plant-disturbing organism attack and planting area of food crops in Indonesia. Clustering of time series data was same as clustering in general, but the distance and method should be able to accommodate time series data structure which was dynamic in time. Evaluation of prediction and forecast mean average percentage error (MAPE) show that forecast of model was performed with clustering as good as forecast of model for each object. However, prediction of model with clustering was not as good as the prediction of model for each object so that the prediction of broad proportion of plant-disturbing organism attack only served as an indicator of the arrival populations of plant-disturbing organism.
The effect of growing place on the oil quality and antioxidant activity of kelor (Moringa oleifera lamk) seed
Authors: Ibrahim N , Nuryanti S , Hasanuddin A , Zubair M S
Published: February 2021

Kelor (Moringa oleifera Lamk) seed is part of the moringa plant that contains vegetable oil and has benefits as cholesterol-lowering agent, coronary heart risk, and cosmetic additives. It can be also used as edible oil and biodiesel oil. This study aims to determine the effect of different growing places on the oil quality and antioxidant activity of Kelor (Moringa oleifera L) seed. The plant was obtained from three different growing places, namely Palu City, Sigi and Parigi Moutong regencies. The determination of the oil quality refers to the Indonesian National Standard (SNI) that includes specific gravity, moisture content, acid value, iodine value, peroxide value, and saponification value. The results showed that Kelor seed oil from Sigi Regency had better quality than other places as indicated by the specific gravity, peroxide value, acid value and saponification value that fulfil the SNI 01-3742-2002 requirements. It also showed the lowest antioxidant activity with an IC50 value of 2.93 mg/ml and the highest level of total phenolic and total flavonoids. This study encourages the application of Kelor seed oil from Sigi Regency as the best raw material for medicine and cosmetics.
Incubation period and agitation speed alter the activity of crude amylase produced by thermophilic bacteria isolated from Pulu Hotspring
Authors: Satrimafitrah P , Razak A R , Indasari  , Tamuntuan D , Irmawati Inda N , Mahfudz M.
Published: February 2021

The purpose of this study was to determine the optimum condition in yielding crude amylase enzymes with the highest activity produced by PL-16, a thermophilic bacteria isolated from Pulu hot spring. The study was carried out using a completely randomized design with a varied incubation time of 0, 6, 12,18, 24, 30, 36, 42, 48, 54, 60, 66, and 72 hours, and agitation speed of 60, 90, 120, 150, 180, and 200 rpm. The activity of extracted crude amylase enzyme was determined using the DNS method and bacterial growth by measuring its Optical Density (OD600). The results showed that optimum amylase activity was at 48 hours incubation time with OD of 2.823 and activity of 0.437 U/mL. Furthermore, agitation speed of 180 rpm produced the highest amylase activity of 0.498 U/mL with an OD of 2.435. Pearson correlation test exhibited a positive and considerable strength of association between enzyme activity and bacterial growth. Our crude amylase showed higher activity after optimizing its production condition with 48 hours incubation and agitation speed of 180 rpm.
Ethanolic extract of local sorghum seedcoat (Sorghum bicolor [L.] Moench) as a potential bacterial growth inhibitor
Authors: Inda N I , Mastura S , Satrimafitrah P
Published: February 2021

Research on the antibacterial activity of ethanol extract from seedcoat of local Sorghum (Sorghum bicolor [L.] Moench) against Staphylococcus aureus and Escherichia coli has been conducted. The purpose of this study was to determine the minimum inhibitory concentration of extract against both gram-positive and negative bacteria. The extraction method used in this study was a maceration method using ethanol as a solvent. Antibacterial activity test was carried out with several concentrations, namely 1 g/mL, 0.8 g/mL, 0.6 g/mL, 0.4 g/mL, 0.2 g/mL, 0.1 g/mL, 0.05 g/mL, 0.025 g/mL, and 0.01 g/mL. The results showed that the extract inhibited bacterial growth in a wide range of concentrations. The smallest inhibition was at a concentration of 0.05 g/mL with a clear zone diameter of 10.29 mm in Staphylococcus aureus and 11.70 mm in Escherichia coli. Ethanol extract of local Sorghum seedcoat exhibited a strong inhibition ability and probably a potential antibacterial agent source.
The comparison of Size-Specific Dose Estimate (SSDE) in chest CT examination calculated based on volumetric CT Dose Index (CTDIvol) reference phantom and Dose Length Product (DLP)
Authors: Binta Y I , Suryani S , Abdullah B
Published: February 2021

The radiation dose of X-ray radiation exposure that comes from a CT scan is not easy to determine, because a CT scan uses several beams of X-ray radiation for one scan. In addition, the X-ray exposure dose on the CT scan is not the sum of the X-ray radiation exposure doses from each beam. Therefore, the Size-SpecificDoseEstimate (SSDE) parameter is used, which means as the estimated radiation exposure dose received by the patient. The SSDE value is a function of the CTDIvol which is determined from the reference phantom or from the derived Dose Length Product (DLP) values. The CTDIvol value determined from the reference phantom has the same value for the weight interval of the patient, so that the patient gets the same dose of X-ray exposure even though the body size is different. The CTDIvol value determined by Dose Length Product (DLP) depends on the lateral length of the patient’s body. Because the lateral body length of each patient is different, the CTDIvol value will be different, so the SSDE value will vary more. The results showed different SSDE values for each patient according to body mass index.
Effect of dustfall pollution on chlorophyl content of polyalthea longifolia leaves
Authors: Asis S A , Suryani S , Assegaf A H
Published: February 2021

Dust has a size of 0.001 - 10000 µm. Dust from motor vehicle exhaust emissions generally has a size of 1 - 150 µm, and flying dust due to vehicle traffic has a size of 100 µm. This dust when it falls on the leaves will cover the stomata. The length of stomata is 80 µm and a maximum width of 50 µm, which is smaller than the size of the dust. As a result, the stomata will be covered by dust, and the leaves cannot absorb CO2, an important ingredient in photosynthesis. The leaf samples used in this study were Polyalthea Longifolia leaves located near the highway, around the cement factory, and in plantations as a control. Then, the leaves were measured for the content of total chlorophyll using the Wintermans and de Mots equation, and mineral content using X-ray Fluorencence (XRF). The results showed that the leaves around the cement factory had a yellow color, small, lower chlorophyll content, and had low absorptivity on minerals than the leaves from near the highway and control.
Effect of extraction method of the eel fish albumin levels of Anguilla marmorata (Q.) gaimard and Anguilla bicolor
Authors: Jamaluddin  , Hasnawati  , Yuyun Y , Pitriani  , Widodo A
Published: February 2021

This study aims to determine the effect of the extraction method based on the type and weight of the sample to the albumin levels, which can produce the highest albumin levels. The extraction method used is heating (hotplate) at heat temperature 60°C for 30 minutes using 0,1 M HCl solvent and steam (water bath) at heat temperature of 60°C (the sample temperature is 45°C) for 30 minutes with distilled water solvent. The weight of the sample used is 0,5 kg and 0,7 kg. Testing of the albumin levels using the Bromocresol green (BCG) method by photometer with a wavelength (λ) 546 nm. According to statistical data the highest albumin levels contained in the extraction of steaming water bath with a value of 0,156 g/100 g. Based on the type of eel, Anguilla marmorata (Q.) Gaimard with the highest albumin levels of 0,143g/100 g. The highest albumin levels of the Anguilla marmorata (Q.) Gaimard on the weight of 0,5 kg and 0,7 kg are 0,132 g/100 g and 0,153 g/100g.
The effect of learning realistic mathematics education (RME) Approach to improve students’ mathematical communication
Authors: Darto
Published: February 2021

Teaching and learning mathematics have predisposition to be conducted mechanically. Teacher prefers to teach algorithm directly, give examples, and then ask the students to finish the tasks similar to those illustrated by the teacher. The students solve a mathematical problem by memorizing without understanding. As a result, the students only master the simple mathematics concepts, but very weak in communication mathematics. This condition can be solved by implementing Realistic Mathematics Education (RME) approach. In the RME approach, there is no special teaching methods required, but from a risen variety of characteristics. This research concludes the learning approach Realistic Mathematics Education (RME) loaded with math activities (doing mathematics), investigation, discussion, and reflection. The research’s subject was 25 students at grade 6 SD Negeri 1 Kecamatan Bunga Raya Kabupaten Siak Riau, Indonesia. Data were collected through observations, interviews, checklist, and analyzing the students’ works .Learning Realistic Mathematics Education (RME) is not a teacher explaining, giving examples,and then the students imitate the example of the teacher, but requiring students to work in groups, investigate, experiment, discuss and share. Realistic Mathematics Education (RME) approach contributes not only to the cognitive development of students but also the affective and psychomotor of students.
Construction Scheme of Proof Based on Assimilation and Accommodation Processes: Theorem of Number Theory
Authors: Hamdani Deni , Subarinah Sri , Triutami Tabita Wahyu , Arjudin  , Sripatmi
Published: February 2021

Number theory is a branch of mathematics that deals with the properties of natural numbers, aimed at finding interesting and unexpected relationships of various types of numbers, then proving that the relationship is true. Problem solutions and proof theorems of number theory often require a strong mathematical background. Weak background, such as: giving examples only applies to definitions not to theorems and lemmas, understanding of the definitions and proven theorems, ways of looking at new theorems built on definitions, and previous theorems, and knowledge of when a definitions or lemma-theorems can be used; enough to affect the ability to solve the problem of number theory. So it is necessary to explore the possibility of other problems in the activity of constructing proof, which in this study uses the framework of the process of assimilation and accommodation, followed by interviews based on student responses. The results obtained are 3 groups of construction schemes, namely: 1) schemes using definitions and theorems, 2) schemes using theorems, and 3) schemes using definitions. Within the three groups there are variations of the problem found that the diantranya occurs is dis-equilibrium in the construction scheme group with theorems and definitions; and group construction schemes by definition only. His advice is to reflect on the ability to understand definitions, theorems and lemmas; and the development of lecture designs that can make the activity of constructing proof into routine activities.
Mathematical Problem-Solving Heuristics Used by Students in College Algebra Class
Authors: Desyarti Safarini TLS , Nurashari Regina , Lie Margaretha Weryana
Published: February 2021

This study is intended to describe heuristics are known and utilized by the first-year university students to solve mathematical problem-solving. Thirty participants were asked to solve two problems. We used the answer sheets to analyze students’ step by step solutions and conduct interview sessions with six selected participants to obtain more in-depth information. 66.67% of the participants were able to solve the first problem and most of them use equation. The other 20% of the participants were able to perform heuristic but fail to get the correct solution, and most of them also used equation heuristic. Some participants also used guess and check and act-it-out heuristic to solve problem 1. In contrast, only 36.67% of participants who were able to solve the second problem, and most of them used guess and check heuristic. About 33.33% of participants were able to perform heuristics but failed to find the correct solution. It was found that most of the participants tend to use heuristics that they had learned when they were in senior high school. They are lack of confidence to use other heuristics.
The need analysis of mathematics module based on problem-based learning to improve reasoning ability
Authors: Lestari Puji , Mardiyana  , Slamet I
Published: February 2021

This study aims to determine how students ‘needs for mathematics modules based on problem-based learning to improve students’ reasoning ability. The research method used is qualitative method. The subjects of this study were teachers and class VIII students in the junior high school even semester of the academic year 2019/2020 which contained 28 students of Junior High School 2 Jumapolo. The analysis was carried out in 3 stages, namely preliminary analysis, student analysis, material teaching analysis. Data collection using non-test methods using interviews and open questionnaires with 10 questions. The results of interviews with mathematics teachers in Junior High School 2 Jumapolo, it is known that students often experience difficulties in learning mathematics. The results of the open questionnaire showed that 82.15% of students stated that the development of mathematics modules based on problem-based learning was needed. The result of teaching materials showed that aspects manipulating mathematics, checking the validity of an argument, drawing conclusions are still not adequate, then for indicators make a conjecture and make generalizations there are still no match. The development of mathematics modules based on problem-based learning is needed to overcome the problem of textbooks that have not yet maximized reasoning ability.
Scaffolding as a strategy to help student difficulties in proving group problems
Authors: Warli  , Rahayu P , Cintamulya I
Published: February 2021

This study aims to describe the use of scaffolding as a teaching strategy in helping students difficulties in proving group problems. To achieve that goal, learning is done with scaffolding strategies on group topics. Then the problem proving test is given on a group topic, then the interview is based on the student’s proof answers. For this reason, this research is qualitative descriptive research. The subjects were students of Unirow Tuban Mathematics education who were taking Algebra Structure Courses. Scaffolding strategy design refers to the mapping of proof indicators, and the level of scaffolding developed by [1] explaining, reviewing and restructuring, and developing conceptual thinking, then associated with the scaffolding strategy developed by [2], including offering explanations, inviting student participation, verifying and clarifying student understanding, modeling desired behavior, and inviting students to contribute instructions. To explore the ability to prove, interview guidelines refer to indicators of construction proof. The results showed that the ability of students in the initial steps of proof, proof of flow, using related concepts, the argument of proof, and the language of proof had a very good increase. So the ability of students to prove after using a strategy to pursue scaffolding has increased. The implication of scaffolding as a strategy becomes an alternative teaching choice in abstract algebra subjects.
Forecasting dengue incidence in Bali by means latent Bayesian count data model
Authors: Chadidjah Anna , Jaya I G N M
Published: February 2021

Dengue disease is a viral infectious disease caused by DENV-1, DENV-2, DENV-3, and DENV-4. It does not only cause a public health problem. However, it may also cause social and economic conditions. Forecasting dengue incidence is a crucial part of an early warning system (EWS), which is needed in controlling and preventing dengue disease. Forecasting is a statistical tool used to obtain future information based on historical data. However, the models had been developed commonly applied for continuous data. It was very rare in modeling count data. We develop a model based on a Poisson log-linear model to accommodate count data. We use the Latent Bayesian approach to estimate the parameters model. We apply the model for forecasting dengue incidence in Bali. We used data from 2011 to 2016 to forecast the dengue incidences in periods 2017-2020. We found a Poisson model with Random Walk order one prior and Half Cauchy hyperprior distribution is the bet model for forecasting dengue incidence in Bali. We found the dengue incidence decrease from 2017 to 2020 where the highest incidence rates always occur from January to May. This condition is thought to be related to the rainfall period.
An Empirical Bayesian Model in Disease Mapping of TB in Regency Bandung
Authors: Marhamah Elin , Padmadisastra S , Suprijadi J
Published: February 2021

In this paper we proposed an Empirical Bayesian model in estimating the relative risk of people getting a Tuberculosis (TB) diseases in a small area.. Which is one of the top rank diseases causing death in Indonesia. It is considered that the proposed model will alleviate some of the draw back that exist in the Standardized Mortality Ratio (SMR) method in estimating relative risk. It is shown that the proposed EB method produce an estimate of the risk which is a weighting averegae of SMR and EB estimate. To give a complete insight into the paper, we give an illustration concerning the case of Tuberculosis (TB), which is still considered as a killer disease in Indonesia.
Applied of feed-forward neural network and facebook prophet model for train passengers forecasting
Authors: Pontoh Resa Septiani , Zahroh S , Nurahman H R , Aprillion R I , Ramdani A , Akmal D I
Published: February 2021

Train is public transportation that is widely used by people in Indonesia. Due to the high level of comfort at a low price relatively. Based on released data by PT KAI, the number of train passengers has increased in almost every holiday season, thereby, it is suspected that there are seasonal patterns with fixed and random periods. In 2013, the government issued a policy related to infrastructure development, then, it caused the number of passengers significantly increasing in the following year. Thus, we need a model that can accommodate these patterns to forecast the number of train passengers accurately. The use of neural network methods such as Feed Forward Neural Network (FFNN), nowadays, becomes popular in facing big data including unexpected fluctuation on the data. Additionally, recently, Facebook announced an accurate method of forecasting, called Prophet model, for data which have trend, seasonality, holidays, and missing data. Hence, the forecast for monthly train passengers on this research is modelled by FFNN and Facebook Prophet. The result shows that Prophet performs better than FFNN. However, the difference in the value of MAPE is not too large.