bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning model inspired by lateral line system for underwater object detection
Authors: Jeong Taekyeong , Yoo Janggon , Kim Daegyoum 
Published: December 2021

Abstract
Inspired by the lateral line systems of various aquatic organisms that are capable of hydrodynamic imaging using ambient flow information, this study develops a deep learning-based object localization model that can detect the location of objects using flow information measured from a moving sensor array. In numerical simulations with the assumption of a potential flow, a two-dimensional hydrofoil navigates around four stationary cylinders in a uniform flow and obtains two types of sensory data during a simulation, namely flow velocity and pressure, from an array of sensors located on the surface of the hydrofoil. Several neural network models are constructed using the flow velocity and pressure data, and these are used to detect the positions of the hydrofoil and surrounding objects. The model based on a long short-term memory network, which is capable of learning order dependence in sequence prediction problems, outperforms the other models. The number of sensors is then optimized using feature selection techniques. This sensor optimization leads to a new object localization model that achieves impressive accuracy in predicting the locations of the hydrofoil and objects with only 40% of the sensors used in the original model.
Use, cost-effectiveness, and end user perspectives of a home solar lighting intervention in rural Uganda: a mixed methods, randomized controlled trial
Authors: Sundararajan Radhika , D’Couto Helen , Mugerwa Joseph , Tayebwa Mellon , Lam Nicholas L , Wallach Eli S , Wiens Matthew O , Ponticiello Matthew , Stanistreet Debbi , Tsai Alexander C , Vallarino Jose , Allen Joseph G , Muyanja Daniel , Shrime Mark G , Nuwagira Edwin , Lai Peggy S 
Published: December 2021

Abstract
Energy poverty is prevalent in resource-limited settings, leading households to use inefficient fuels and appliances that contribute to household air pollution. Randomized controlled trials of household energy interventions in low and middle income countries have largely focused on cooking services. Less is known about the adoption and impact of clean lighting interventions. We conducted an explanatory sequential mixed methods study as part of a randomized controlled trial of home solar lighting systems in rural Uganda in order to identify contextual factors determining the use and impact of the solar lighting intervention. We used sensors to track usage, longitudinally assessed household lighting expenditures and health-related quality of life, and performed cost-effectiveness analyses. Qualitative interviews were conducted with all 80 trial participants and coded using reflexive thematic analysis. Uptake of the intervention solar lighting system was high with daily use averaging 8.23 ± 5.30 h d−1. The intervention solar lighting system increased the EQ5D index by 0.025 (95% CI 0.002–0.048) and led to an average monthly change in household lighting costs by −1.28 (−2.52, −0.85) US dollars, with higher savings in users of fuel-based lighting. The incremental cost-effectiveness ratio for the solar lighting intervention was $2025.72 US dollars per quality adjusted life year gained making the intervention cost-effective when benchmarked against the gross domestic product per capita in Uganda. Thematic analysis of qualitative data from individual interviews showed that solar lighting was transformative and associated with numerous benefits that fit within a social determinants of health (SDOH) framework. The benefits included improved household finances, improved educational performance of children, increased household safety, improved family and community cohesion, and improved perceived household health. Our findings suggest that household solar lighting interventions may be a cost-effective approach to improve health-related quality of life by addressing SDOH.
Superior Nitrogen Fixing Bacteria Screening from Various Rhizobiome in Palm Oil Plantion, North Sangatta, East Kalimantan
Authors: Anggrainy Eka Dewi , Hidayati Arifah , Syarifain Roby Ibnu , Zulkarnain Muhammad Faizal Rezha , Simarmata Tualar 
Published: December 2021

Abstract
Nitrogen fixing bacteria (NFB) plays an important role in increasing N availability for plants. Research to examine the ability of nitrogen fixing bacteria isolates to produce nitrogenase, phytohormone and the ability of nitrogen fixing bacteria isolates in the biological test process using the corn plant indicator as an indicator has been carried out from September 2018 to February 2019 in laboratories and greenhouses. The ability of nitrogen fixing bacteria was tested by the ARA method, while the phytohormone testing of nitrogen fixing bacteria was tested using the HPLC method. Bioassays using Murphy media and corn plants as indicators were performed using a randomized block design consisting of six treatments (one control and five selected NFB isolates from the selection results) and given five replications. Measurement of root length, plant height, and dry weight of plants were carried out every 2 days for 14 days. The results showed that the nitrogen fixing bacteria isolates used from North Sangattarhizobiome, East Kalimantan had different nitrogenase and differentphytohormone test results, and obtained five selected isolates based on the selection results. The results of the bioassay did not show any significant differences based on the Duncan test at the level of 5%. However, it can be seen visually the significant difference in which plants in the biological test using nitrogen fixing bacterial isolates have relatively higher plant growth and dry weight of plants than plants that are not given treatment or control.
Rainfall trend analysis using Mann-Kendall and Sen’s slope estimator test in West Kalimantan
Authors: Aditya F , Gusmayanti E , Sudrajat J 
Published: December 2021

Abstract
Climate change has been a prominent issue in the last decade. Climate change on a global scale does not necessarily have the same effect in different regions. Rainfall is a crucial weather element related to climate change. Rainfall trends analysis is an appropriate step in assessing the impact of climate change on water availability and food security. This study examines rainfall variations and changes at West Kalimantan, focusing on Mempawah and Kubu Raya from 2000-2019. The Mann-Kendall (MK) and Sen's Slope estimator test, which can determine rainfall variability and long-term monotonic trends, were utilized to analyze 12 rainfall stations. The findings revealed that the annual rainfall pattern prevailed in all locations. Mempawah region tends to experience a downward trend, while Kubu Raya had an upward trend. However, a significant trend (at 95% confidence level) was identified in Sungai Kunyit with a slope value of -33.20 mm/year. This trend indicates that Sungai Kunyit will become drier in the future. The results of monthly rainfall analysis showed that significant upward and downward trends were detected in eight locations. Rainfall trends indicate that climate change has occurred in this region.
The Regional Hadley Cells Response to the Sea Surface Temperature Distribution Across the Indo-Pacific Ocean
Authors: Fatmasari D 
Published: December 2021

Abstract
Hadley Cells are thermally driven cell in the tropics. On its occurrence, these cells are strongly influenced by the sea surface temperature (SST) distribution across the tropical ocean or the Pacific Ocean as the investigated location in this study. The SST shifting in the Pacific Ocean is mainly due to the ENSO. An opposite SST polarity between the western and eastern Pacific Ocean are captured during ENSO events. This means that ENSO could trigger an anomalous regional Hadley Cells that behave oppositely between Indonesia or the western Pacific and the eastern Pacific. This study examines the strength of the regional Hadley Cells related to the ENSO event across the Indonesian region and the Pacific Ocean. A significant correlation between the Hadley Cells and ENSO as the tropical climate variability in the Pacific Oceans are found. The strength of the Hadley Cells associated with ENSO event is examined by using the zonally average vertical velocity across the Pacific Ocean. During La Nina, the regional Hadley Cells over Indonesia or the western Pacific strengthened, whereas the regional cells over the eastern Pacific weakened. In contrast, during El Nino where the warm pool shifted to the eastern Pacific, the regional cell in the eastern Pacific strengthened, while the cell over the western Pacific weakened. These anomalous conditions clearly show that the meridional temperature gradient is strongly affecting the regional Hadley Cells strength. The stronger the meridional temperature gradient, the stronger the regional Hadley Cells.
Spatio-Temporal Lightning Analysis Over North Sulawesi in 2019 - 2020
Authors: Azani A A , Pratama D A , Kusumawardani N , Rusmala I , Septiadi D 
Published: December 2021

Abstract
This study aimed to analyze the spatial and temporal lightning distribution in North Sulawesi. The general meteorological condition of North Sulawesi has also been considered to identify the cause of the lightning occurrence. Lightning activity over North Sulawesi has been investigated using lightning data from Winangun Geophysical Station during 2019-2020. The result shows that in the land area of North Sulawesi, the highest lightning density occurred in the Tomohon regions due to its topographical features. Overall, the flash density over the land area is higher than the sea area due to its high atmospheric instability. The maximum flash density does not occur during the wet periods, but it occurs during September - October – November, which has a high surface temperature over this region. It is also known that shearline and low-pressure areas contribute to the high lightning occurrence in October 2019, while La Nina in the Pacific Ocean influences lightning activity in July 2020
The Determination of Rainfall Threshold Triggering Landslides Using Remote Sensing
Authors: Agustina L , Safril A 
Published: December 2021

Abstract
Landslide is one of the natural disasters that can cause a lot of loss, both material and fatalities. Banjarnegara Regency is one of Central Java Province regencies where landslides often occur due to the region's topography and high intensity rainfall.. Therefore, it is necessary to determine the threshold of rainfall that can trigger landslides to be used as an early warning for landslides. The rainfall data used for the threshold is daily and hourly rainfall intensity from remote sensing data that provides complete data but relatively rough resolution. So that remote sensing data need to be re-sampled. The remote sensing data used is CMORPH satellite data that has been re-sampled for detailing existing information of rainfall data. The resampling method used is the bilinear method and nearest neighbor by choosing between the two based on the highest correlation. Threshold calculation using Cumulative Threshold (CT) method resulted equation P3 = 7.0354 - 1.0195P15 and Intensity Duration (ID) method resulted equation I = 1.785D-0305. The peak rainfall intensity occurs at the threshold of 97-120 hours before a landslide occur.
Analysis of variability and projection of extreme rainfall in West Java
Authors: Hutauruk R C H , Amin T , Irawan A M 
Published: December 2021

Abstract
This research discusses the effect of climate change on extreme rainfall in West Java using the RCP 4.5 and RCP 8.5 scenarios by comparing daily rainfall data with model ACCESS-1, CSIROMK3.6 model, MIROC-5 from NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) and the ensemble of three models each season with Extreme Dependency Score (EDS) method. This study projects an extreme rainfall index of 30 years (2011-2040). The three extreme rainfall indices issued by the Expert Detection Team and the Climate Change Index (ETCCDI) consisted of Rx1day, R50mm, and R95p used in this study. The results showed that the projection period (2011-2040) used RCP 8.5 which had a trend of increasing extreme rain index that was greater than RCP 4.5. For RCP 8.5 the maximum rainfall will increase in Indramayu, Majalengka, Purwakarta, Sukabumi and Ciamis areas. Increased rainy days occurred in Bogor, Bekasi, Karawang, Purwakarta, Bandung, Sumedang, Majalengka, Cirebon, Indramayu. Extreme rainfall will increase in Bekasi, Karawang and Bogor regions.
Spatiotemporal variation of mesoscale convective system type persistent elongated convective system (PECS) in Indonesia
Authors: Firdaus I M , Trilaksono N J 
Published: December 2021

Abstract
Research on Mesoscale Convective Systems (MCS) in Indonesia has been studied to identify the characteristics and distribution of MCS. Persistent Elongated Convective System (PECS) is one of the MCS types that can cause extreme weather. However, identifying the spatial and temporal variations of PECS in Indonesia has not been studied further. This study examines the Persistent Elongated Convective System (PECS) in Maritime Continent from 2010-2014. MERG Dataset with brightness temperature is used to identify PECS and input Grab ‘Em Tag ‘Em Graph ‘Em (GTG) tracking algorithm to gain a better understanding of spatiotemporal variation in Indonesia. Flood data from BNPB in Indonesia is used to find out the correlation between flood and PECS events in Indonesia. PECS events in 2010-2014 are 5574 events, which the transitional period (MAM and SON) has the most PECS events and longest duration of life. The PECS spread in the early morning on land and sea, while on day and nighttime, it is concentrated on land for the area of Java, Papua, and Southern Kalimantan. Java and Papua have the largest concentrated PECS events. The life phase of the PECS has two peaks or semidiurnal cycles, in the morning and nighttime. There are differences in peak times for each phase of the PECS on land and sea. In Indonesia, PECS has the smallest average maximum area than in the USA and China but has the longest duration and largest eccentricity. Based on flood data from BNPB, during 2010-2014, Java has the most flood. According to flood data and PECS events in Java, there is 6.72% of flood coincided with PECS events during the period.
The characteristics of the equatorial waves caused a record torrential rain event over western Sumatra
Authors: Wu P , Fukutomi Y , Kikuchi K 
Published: December 2021

Abstract
This study examined the cause of a record torrential rain event over the western coast of Sumatra Island in March 2016. The influence of atmospheric equatorial waves (EWs) and the characteristics of the EWs were investigated. Analysis of the Japanese 55-year Reanalysis data (JRA-55) and precipitation data from the Global Precipitation Measurement (GPM) satellite showed that the event was caused by the combined effects of Kelvin waves, equatorial Rossby waves, and westward inertio-gravity (WIG) waves. An examination of the characteristics of the EWs revealed that the Kelvin waves had longitudinal scales of ~6,000 km, with a period of ~6 days and phase speed of ~12 m s-1, which was typical of the convectively coupled Kelvin waves in this region. The WIG waves had a scale of ~2,500 km, with a period of 2.5 days and a relatively fast phase speed of 12~13 m s-1. Heavy precipitation occurred when an eastward Kelvin wave from the Indian Ocean encountered a westward inertio-gravity (WIG) over Sumatra Island. It was concluded that along with the Kelvin and equatorial Rossby waves, the WIG waves might have played a major role in the formation of the extreme precipitation event.
Meridional Migration of Diurnal Heavy Rainfall during Extreme Events over Java and Surrounding Waters and Its Relation to Madden Julian Oscillation
Authors: Rais A. F. , Kosasih A. , Sujarwo  , Fitrianto M. A. , Kamid A. , Surgiansyah P. 
Published: December 2021

Abstract
In this study, meridional migration characteristic of diurnal heavy rainfall (DHR) over Java and surrounding waters and its relation to Madden Julian Oscillation (MJO) during extreme events was investigated. The rainfall data was the Climate Prediction Centre Morphing (CMORPH) V1.0 in the December-January-February (DJF) wet season of the 1998-2019 period. The thresholds of extreme events were based on the 95% percentile of daily rainfall area average of mountainous area (MA), northern plain area (NPA), northern waters (NW), southern plain area (SPA), and southern waters (SW) that were based on Global Land One-kilometer Base Elevation (GLOBE) Digital Elevation Model. We analyzed meridional migration of DHR through composite and the Hovmoller diagram. To get the MJO signal, we used the Wheeler-Kiladis wavenumber-frequency domain to filter outgoing longwave radiation (OLR). The results showed that DHR was stationary over the mountains, migrated to the Java Sea (The Indian Ocean), and was stationary over the Java Sea (Indian Ocean) in conjuction with migration from Java to the waters when extreme events occur over MA, NPA (SPA), and NW (SW), respectively. Based on a comparison of MJO-OLR during extreme events period of MA, NPA, SPA, NW, and SW, it seems that MJO had a stronger impact on the DHR of NW and SW than the others, but it must be examined based on significant test in the further study.
Utilization of Red-Green-Blue (RGB) modification composite for nighttime convective cloud monitoring over North Sumatra region
Authors: Saragih I J A , Tarigan K , Sinambela M , Situmorang M , Sembiring K , Humaidi S , Sirait M 
Published: December 2021

Abstract
Located between the Indian Ocean and the Malacca Strait, also the presence of the Bukit Barisan Mountains cause high convective activity in the North Sumatra region. The Himawari-8 satellite has 16 atmospheric observation channels that allow for observations of the convective system growth phase. The Red-Green-Blue (RGB) composite method is used to display a variety of satellite image composite information. The nocturnal convective system that often forms in the coastal areas of Sumatra causes heavy rains. A nocturnal convective system observation method is needed to publish early warning information on extreme weather. This research was conducted to observe the nocturnal convective system during heavy rain events in the North Sumatra region using a modification of RGB composite. This research used the Himawari-8 satellite data, Coloumn Max (CMAX) products of Medan weather radar data, and Global Satellite Mapping of Precipitation (GSMaP) rainfall estimation data. Comparison of RGB modified products with Night Microphysics RGB products and CMAX weather radar products, as well as time-series rainfall analysis. The results showed that the RGB modification product could capture the beginning of the convective system's growth, development, and spatial movement. The convective cloud distribution pattern corresponds to the area of heavy rain. There is a slight difference in cloud growth area between the satellite and radar products indicated the parallax error from the satellite image.
Estimation of Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Using GSSHA Model (Case Study Area Upper Citarum Watershed)
Authors: Sibuea Puji R A , Agriamah Dewi R , Riawan Edi , Suwarman Rusmawan , Lubis Atika 
Published: December 2021

Abstract
Probable Maximum Flood (PMF) used in the design of hydrological structures reliabilities and safety which its value is obtained from the Probable Maximum Precipitation (PMP). The objectives of this study are to estimate PMP and PMF value in Upper Citarum Watershed and understand the impact from different PMP value to PMF value with two scenarios those are Scenario A and B. Scenario A will calculate the PMP value from each Global Satellite Mapping of Precipitation (GSMaP) rainfall data grid and Scenario B calculate the PMP value from the mean area rainfall. PMP value will be obtained by the statistical Hershfield method, and the PMF will be obtained by employed the PMP value as the input data in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model. Model simulation results for PMF hydrographs from both scenarios show that spatial distribution of rainfall in the Upper Citarum watershed will affect the calculated discharge and whether Scenario A or B can be applied in the study area for PMP duration equal or higher than 72 hours. PMF peak discharge for Scenario A is averagely 13,12% larger than Scenario B.
Examining optimum prediction time of rainfall dynamics based on chaotic perspective at different temporal scales: a case study in Bojonegoro, Indonesia
Authors: Hidayat A M , Efendi U , Virgianto R H , Nugroho H A 
Published: December 2021

AbstractAs the driving force of the hydrological system, rain has severe impact when dealing with petroleum mining activities, especially in protecting assets and safety. Rainfall has high variability, both spatial and temporal (chaotic data). Due to this reason, ones can only create long-range prediction using the stochastic method. Here we use the Lyapunov exponent to analyze the nonlinear pattern of rainfall dynamics. This method is useful for identifying chaotic deportment in rainfall data. This study uses rainfall data for six years obtained from one of the largest petroleum mining sites in Bojonegoro, Indonesia. Rainfall dynamics have been analyzed on three different time scales, namely daily data, 5-day, and 10-day. The time delay (τ) was obtained by using the Average Mutual Information (AMI) method for the three-rainfall series (3, 2, 3, respectively). The observed rainfall data in Bojonegoro show signs of chaos as the finite correlation dimensions (m) attain values about 4 for all time scales. The maximum Lyapunov exponentλmaxfor each of three-rainfall series in Bojonegoro is 0.111, 0.057, 0.062, respectively. These values were analyzed to find the optimum prediction time of rainfall occurrence to perform better forecasting. The result shows that the optimum range of prediction time for daily, 5-day, and 10-day have 9, 18, and 16 times longer than their temporal scale.
Monthly daily-mean rainfall forecast over Indonesia using machine learning and artificial intelligence ensemble
Authors: Harsa H , Habibie M N , Praja A S , Rahayu S P , Hutapea T D , Swarinoto Y , Kurniawan R , Noviati S 
Published: December 2021

Abstract
A daily mean rainfall in a month forecast method is presented in this paper. The method provides spatial forecast over Indonesia and employs ensemble of Machine Learning and Artificial Intelligence algorithms as its forecast models. Each spatial grid in the forecast output is processed as an individual dataset. Therefore, each location in the forecast output has different stacked ensemble models as well as their model parameter settings. Furthermore, the best ensemble model is chosen for each spatial grid. The input dataset of the model consists of eight climate data (i.e., East and West Dipole Mode Index, Outgoing Longwave Radiation, Southern Oscillation Index, and Nino 1.2, 3, 4, 3.4) and monthly rainfall reanalysis data, ranging from January 1982 until December 2019. There are four assessment procedures performed on the models: daily mean rainfall establishment as a response function of climate patterns, and one-up to three-month lead forecast. The results show that, based on their performance, these non-Physical models are considerable to complement the existing forecast models.