KnE Life Sciences最新文献_第2页

Potential of Solar Energy Mapping in East Priangan Using Satellite Imagery and Environmental Based on GIS 利用卫星图像和基于地理信息系统的环境，绘制东普里扬甘地区太阳能潜力图

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15592

Riki Purnama Putra, Seni Susanti, Indy Ramadhanti, R. D. Agustina

{"title":"Potential of Solar Energy Mapping in East Priangan Using Satellite Imagery and Environmental Based on GIS","authors":"Riki Purnama Putra, Seni Susanti, Indy Ramadhanti, R. D. Agustina","doi":"10.18502/kls.v8i1.15592","DOIUrl":"https://doi.org/10.18502/kls.v8i1.15592","url":null,"abstract":"Renewable energy is an energy that can be used to turn on all the energy that is still widely used in the world, including in Indonesia. Solar energy is a renewable energy that uses solar energy as the main ingredient in the formation of electrical energy. Solar energy is one of the most likely energies in a country that is on the equator like Indonesia. One of the interesting problems is how to determine the most effective area for the installation of solar power plants to make the power received by the power plant more effective. This study aims to analyze the effective area for installing solar panels using a Geographic Information System (GIS) as well as mapping of Centralized Solar Power (CSP) and centralized solar photovoltaic (SPV) in the East Priangan area, West Java. The method used in this study is based on the use of remote sensing of the average annual horizontal irradiation (GHI) and Normal Direct Irradiation (DNI). Solar irradiation data (GHI and DNI) were obtained from data from the surface meteorological program and solar energy by NASA, while Land Use/Land Cover, and Digital Elevation Models were used with the use of GIS. The results show that high areas in East Priangan get more effective CSP and SPV results than low areas, but low areas show an average effectiveness value in denuded areas. \u0000Keywords: solar energy, east Priangan, satellite imagery, environmental, GIS","PeriodicalId":17898,"journal":{"name":"KnE Life Sciences","volume":"22 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of Flowing Electrolyte Lead Acid Battery Operating Voltage 识别流动电解液铅酸蓄电池的工作电压

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15589

Kurriawan Budi Pranata, Hari Lugis Purwanto, M. Ghufron, Istiroyah, Muhammad Priyono Tri Sulistyanto, Anggri Sartika Wiguna, Sulthoni Akbar, Fauzan Azhiman

{"title":"Identification of Flowing Electrolyte Lead Acid Battery Operating Voltage","authors":"Kurriawan Budi Pranata, Hari Lugis Purwanto, M. Ghufron, Istiroyah, Muhammad Priyono Tri Sulistyanto, Anggri Sartika Wiguna, Sulthoni Akbar, Fauzan Azhiman","doi":"10.18502/kls.v8i1.15589","DOIUrl":"https://doi.org/10.18502/kls.v8i1.15589","url":null,"abstract":"Identification of the operating voltage of a lead acid battery with 30% sulfuric acid electrolyte flow has been carried out. The battery consists of six cells with Pb and PbO as electrodes. The battery is equipped with a 1200 ml reservoir system to collect electrolyte and supply electrolyte to each cell. Each cell has electrolyte inlets and outlets at the top and bottom that circulate through each cell using a peristaltic pump. The battery prototype built was tested for five charge-discharge cycles with a constant current of 2 A for the charging process and 0.5 A for the discharging process using Turnigy Accucell. During the charge-discharge cycle test, monitoring and recording of voltage data is carried out using a Laptop PC. Data processing uses WebplotDigitizer and Microsoft Excel for data graphing. The results are analyzed and used to identify the operating voltage of the battery by taking the average voltage over five charge-discharge cycles. The average voltage is 13.98 V for the charging process and 12.11 V for the discharging process. Six-cell battery with full capacity works at a voltage range of 12.11-13.98 V. In the process of charging with a constant current of 2 A, the battery takes an average of 7.49 hours. So, the charging capacity can be estimated at 14,980 mAh. Whereas the battery discharge process takes an average of 11 hours with a constant current of 0.5 A to a voltage drop of 10.81 V. The resulting capacity of the discharged battery is 5500 mAh. \u0000Keywords: flowing electrolyte, lead acid battery, operating voltage","PeriodicalId":17898,"journal":{"name":"KnE Life Sciences","volume":"16 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison Test Between Amrita Virtual Lab and Real Spectrometer on Refractive Index Using Blended Laboratory 使用混合实验室进行阿姆里塔虚拟实验室与真实光谱仪在折射率方面的对比测试

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15621

Indy Ramadhanti, Riki Purnama Putra, Shidiq Andhika, Roprop Latiefatul Millah, R. D. Agustina, M. Listiawati

{"title":"Comparison Test Between Amrita Virtual Lab and Real Spectrometer on Refractive Index Using Blended Laboratory","authors":"Indy Ramadhanti, Riki Purnama Putra, Shidiq Andhika, Roprop Latiefatul Millah, R. D. Agustina, M. Listiawati","doi":"10.18502/kls.v8i1.15621","DOIUrl":"https://doi.org/10.18502/kls.v8i1.15621","url":null,"abstract":"Education in the 21st century is an era where learning is unconventional. Interactive learning in the 21st century can be done by conducting virtual or real laboratory activities, even by combining the two in one activity at once. Various innovations in virtual laboratories have spread to optical materials, especially refraction by using a virtual spectrometer. Conceptually, the refractive index is a measure of the bending ray of a light beam as it passes from one medium to another. The refractive index is given by measurement between the refractive index of air, the angle of the prism, and the angle of minimum deviation. The angle of the prism and the angle of minimum deviation can be measured with a spectrometer. The spectrometer is a scientific instrument used to separate and measure the spectral components of physical phenomena and can separate white light and measure individual narrow color bands. Other than an on-hand spectrometer, other tools that we can use to measure the angle of minimum deviation are by using a virtual spectrometer provided by several virtual labs. The study aimed to compare the result of refractive index between on on-hand spectrometer and a virtual lab. Here we report our study on spectrometer whether the virtual lab experiment yields the same results as the real lab. We compare both results of experimental data using data and graph analytics. The results of the study show that the difference in the index of refraction measured between the virtual lab and the real lab is about 0.2%. This shows that there is no significant difference between virtual lab and real lab. \u0000Keywords: amrita virtual lab, real spectrometer, refractive index, blended laboratory","PeriodicalId":17898,"journal":{"name":"KnE Life Sciences","volume":"34 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140377032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Generalized Reciprocal Method on 2D Seismic Refraction Data in Mt. Manglayang, West Java 广义互易法在西爪哇芒格莱阳山二维地震折射数据中的应用

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15619

Yosi Dinar Nugerahani, F. H. Muhammad, R. Wiratama, Imamal Muttaqien, R. D. Agustina

{"title":"Application of Generalized Reciprocal Method on 2D Seismic Refraction Data in Mt. Manglayang, West Java","authors":"Yosi Dinar Nugerahani, F. H. Muhammad, R. Wiratama, Imamal Muttaqien, R. D. Agustina","doi":"10.18502/kls.v8i1.15619","DOIUrl":"https://doi.org/10.18502/kls.v8i1.15619","url":null,"abstract":"The imaging of shallow subsurface structures, weathered rock thickness and velocity propagation distribution of the rocks can be identified by seismic refraction. This method is one of the geophysical exploration methods utilizing refracted wave once it reaches the boundary of subsurface layer. In this research we used the generalized reciprocal method (GRM) as one of the robust processing methods in analyzing subsurface data. This method was chosen due to its accuracy in interpreting shallow subsurface layer with highly undulating refractors by determining time velocity analysis, XY optimum distances, and time depth analysis, then, the expected depth values can be achieved. The acquisition of data for this research was conducted using 13 geophones with forward and reverse sources, the data were then picked to get travel time values and inverted to obtain real geological setting of the earth. The results were interpreted as 2 layers, the first layer had a velocity distribution of 499.289 m/s which was identified as a weathered layer with a thickness of about 4.74 meters, whereas the second layer was interpreted as clay rock with velocity distribution of 1270.433 m/s with the thickness reached up to 16 meters. \u0000Keywords: generalized reciprocal method, 2D seismic refraction","PeriodicalId":17898,"journal":{"name":"KnE Life Sciences","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementing Time Series Cross Validation to Evaluate the Forecasting Model Performance 利用时间序列交叉验证评估预测模型性能

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15584

W. Sulandari, Y. Yudhanto, Sri Subanti, E. Zukhronah, Muhammad Zidni Subarkah

{"title":"Implementing Time Series Cross Validation to Evaluate the Forecasting Model Performance","authors":"W. Sulandari, Y. Yudhanto, Sri Subanti, E. Zukhronah, Muhammad Zidni Subarkah","doi":"10.18502/kls.v8i1.15584","DOIUrl":"https://doi.org/10.18502/kls.v8i1.15584","url":null,"abstract":"Theoretically, forecast error increases as the forecast horizon increases. This study aims to assess whether the statement is generally accepted or not. This study applies time series cross-validation to evaluate forecasting results up to seven steps ahead. As an illustration, we use Malaysia’s hourly electricity load data. Each hour is considered a series of each, so there are 24 daily series. Time series cross-validation with a 334 window was applied to 24 data series, and then each daily series was modeled with the Autoregressive Integrated Moving Average (ARIMA), Neural Network Autoregressive (NNAR), ExponenTial Smoothing (ETS), Singular Spectrum Analysis (SSA), and General Regression Neural Network (GRNN) models. In terms of mean absolute percentage error (MAPE) from one to seven steps ahead, we then evaluate the performance of all models. The experimental results show that the MAPEs obtained from the GRNN model tend to increase along with the theory. However, MAPEs obtained from ETS increase by up to three steps ahead and decrease after that. Among the five models, ARIMA, NNAR, and SSA produce a reasonably stable MAPE value for one to seven steps ahead. However, SSA has the most stable error value compared to ARIMA and NNAR. \u0000Keywords: time series, cross-validation, evaluate, forecasting model performance","PeriodicalId":17898,"journal":{"name":"KnE Life Sciences","volume":"40 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting the Export Unit Value Index in Indonesia Using the Single Input Transfer Function 利用单一输入转移函数预测印度尼西亚的出口单位价值指数

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15581

Ika Fitria Millenia, E. Zukhronah, W. Sulandari

{"title":"Forecasting the Export Unit Value Index in Indonesia Using the Single Input Transfer Function","authors":"Ika Fitria Millenia, E. Zukhronah, W. Sulandari","doi":"10.18502/kls.v8i1.15581","DOIUrl":"https://doi.org/10.18502/kls.v8i1.15581","url":null,"abstract":"Export is one of the factors that increase the economic growth of a country. One measure of export activity that can describe economic growth in Indonesia is The Export Unit Value Index, which is an index that measures changes in the price of export commodities sold by residents of one country to residents of other countries. The purpose of this study is to predict the unit value index of exports in Indonesia using a single input transfer function model and to see the influence of the value of oil and gas and non-oil and gas exports on the unit value index of exports in Indonesia. The single input transfer function model is a model that describes the future forecast of a series (output series) obtained based on the past values of the output series and other time series (input series) that affect the output series. The results of this study obtained a transfer function model with the order (0,0,1) with a noise series following ARIMA (1,0,1). Based on this model, the export unit value index at time t is influenced by the unit value export index in the previous month and is influenced by the oil and gas and non-oil and gas export value in the same month. As indicated by its MAPE value of 4.89%, the forecast value does not diverge much from the actual value, which suggests that the transfer function model can be used to predict the export unit value index in Indonesia. \u0000Keywords: forecasting, export unit value index, single input, transfer function","PeriodicalId":17898,"journal":{"name":"KnE Life Sciences","volume":"16 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140374683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Making Briquettes Variation Ingredient Durian Peel, Husk Rice, and Shell Coconut -- Impact on Strength, Burnability, Temperature, and Calorific Value 制作煤砖的不同成分榴莲皮、稻壳和椰子壳 -- 对强度、可燃性、温度和热值的影响

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15594

I. Utami, Dadi Rusdiana, Nahadi, Irma Rahma Suwarma

引用次数: 0

Isolation of Dark Septate Endophyte (DSE) from Ferns (Pteris Vittata) Roots 从蕨类植物（Pteris Vittata）根中分离出暗色内生菌（DSE）

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15403

Dalia Sukmawati, Alza Kirana Thaharah, Esti Komariah, Herawati Herawati, Vicky Theodora, A. Supiyani, Shabrina Nida Al Husna, N. Ratnaningtyas, H. E. El Enshasy, D. Dailin

{"title":"Isolation of Dark Septate Endophyte (DSE) from Ferns (Pteris Vittata) Roots","authors":"Dalia Sukmawati, Alza Kirana Thaharah, Esti Komariah, Herawati Herawati, Vicky Theodora, A. Supiyani, Shabrina Nida Al Husna, N. Ratnaningtyas, H. E. El Enshasy, D. Dailin","doi":"10.18502/kls.v8i1.15403","DOIUrl":"https://doi.org/10.18502/kls.v8i1.15403","url":null,"abstract":"Dark Septate Endophytes (DSE) are a group of ascomycetes that live in intracellular and extracellular root tissue to facilitate plant growth and stress tolerance in extreme environments. However, little is known about the DSE fungi isolated from certain plant roots such as Pteris vittata, especially under drought condition. Pteris vittata is known for its ability to live in various types of substrates and ecosystems. In this study, we obtained DSE fungi from the fern roots Pteris vittata collected from the area of Universitas Negeri Jakarta. DSE isolation was carried out by inoculating the Pteris vittata fern roots with a size of 0.5 cm on the surface of sterilized PDA media for 5-7 days at 27∘C. Observations were made every 24 hours using a stereo microscope to see the first hyphae appeared from the plant roots. The results exhibited 13 samples of roots with hyphae colonization and were suspected to be DSE fungi. Of the 13 root isolates, only 3 isolate (PP2, PP4A, and PPB) showed the DSE growth (23%) with melanin pigment. The morphological characteristics of endophytic DSE fungi collected from Pteris vittata roots represented septate hyphae, brownish to black colony color, a growing zone, and a velvety texture. For the isolate PP2, it showed sclerotia while for the isolate PP4, it exhibited light brown colonies. \u0000Keywords: isolation, DSE, ferns, pteris vittata, roots","PeriodicalId":17898,"journal":{"name":"KnE Life Sciences","volume":"14 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameter Estimation and Hypothesis Testing on Bivariate Log-Normal Regression Models 双变量对数正态回归模型的参数估计和假设检验

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15546

Kadek Budinirmala, Purhadi, Achmad Choiruddin

引用次数: 0

Community Perceptions on Conserving Watershed Areas 社区对保护流域地区的看法

KnE Life Sciences Pub Date : 2024-03-27 DOI: 10.18502/kls.v8i1.15611

Rendi Zulni Ekaputri, H. Surtikanti, Topik Hidayat, Wahyu Surakusumah

引用次数: 0