Abstract. Current estimates of carbon (C) storage in peatland systems worldwide indicate that tropical peatlands comprise about 15 % of the global peat carbon pool. Such estimates are uncertain due to data gaps regarding organic peat soil thickness, volume and C content. We combined a set of indirect geophysical methods (ground-penetrating radar, GPR, and electrical resistivity imaging, ERI) with direct observations using core sampling and C analysis to determine how geophysical imaging may enhance traditional coring methods for estimating peat thickness and C storage in a tropical peatland system in West Kalimantan, Indonesia. Both GPR and ERI methods demonstrated their capability to estimate peat thickness in tropical peat soils at a spatial resolution not feasible with traditional coring methods. GPR is able to capture peat thickness variability at centimeter-scale vertical resolution, although peat thickness determination was difficult for peat columns exceeding 5 m in the areas studied, due to signal attenuation associated with thick clay-rich transitional horizons at the peat-mineral soil interface. ERI methods were more successful for imaging deeper peatlands with thick organomineral layers between peat and underlying mineral soil. Results obtained using GPR methods indicate less than 3 % variation in peat thickness (when compared to coring methods) over low peat-mineral soil interface gradients (i.e., below 0.02 • ) and show substantial impacts in C storage estimates (i.e., up to 37 MgC ha −1 even for transects showing a difference between GPR and coring estimates of 0.07 m in average peat thickness). The geophysical data also provide information on peat matrix attributes such as thickness of organomineral horizons between peat and underlying substrate, the presence of buried wood, buttressed trees or tip-up pools and soil type. The use of GPR and ERI methods to image peat profiles at high resolution can be used to further constrain quantification of peat C pools and inform responsible peatland management in Indonesia and elsewhere in the tropics.
Abstract. Current estimates of carbon (C) storage in peatland systems worldwide indicate tropical peatlands comprise about 15% of the global peat carbon pool. Such estimates are uncertain due to data gaps regarding organic peat soil thickness and C content. Indonesian peatlands are considered the largest pool of tropical peat carbon (C), accounting for an estimated 65% of all tropical peat while being the largest source of carbon dioxide emissions from degrading peat worldwide, posing a major concern regarding long-term sources of greenhouse gases to the atmosphere. We combined a set of indirect geophysical methods (ground penetrating radar, GPR, and electrical resistivity imaging, ERI) with direct observations from core samples (including C analysis) to better understand peatland thickness in West Kalimantan (Indonesia) and determine how geophysical imaging may enhance traditional coring methods for estimating C storage in peatland systems. Peatland thicknesses estimated from GPR and ERI and confirmed by coring indicated variation by less than 3% even for small peat-mineral soil interface gradients (i.e. below 0.02°). The geophysical data also provide information on peat matrix attributes such as thickness of organomineral horizons between peat and underlying substrate, the presence of wood layers, buttressed trees and soil type. These attributes could further constrain quantification of C content and aid responsible peatland management in Indonesia.
Stocks, apart from having volatile and chaotic characteristics, also have various kinds of noise, non-linear and non-stationary movements, making them difficult to predict accurately. Therefore, the risk of investing in stocks depends on the skills of investors or traders in making judgments and decisions. This study aims to use Long Short-Term Memory (LSTM) as a decision-making technique with historical stock prices as the sole predictor, then implement it in conditions before and during the COVID-19 pandemic. The study results concluded that Long Short-Term Memory (LSTM) could be used as a decision-making technique in conditions before and during the COVID-19 pandemic with historical price inputs as the sole predictor. Based on the research that has been done, the following conclusions can be drawn: The LSTM model can predict stock prices well using historical stock prices as the sole predictor. The LSTM model can be used as a trading decision-making technique for day traders. The risk of stock prediction using the LSTM method in 2019 before the COVID pandemic was proven to be lower than in 2020 during the COVID pandemic. For further research, researchers can conduct more in-depth research on the risk criteria for making trading decisions as an essential reference that can be used to select the LSTM model.
Cooperatives function to build and develop the potential economic capacity of members to improve economic and social welfare. Cooperatives are established aiming to realize the welfare of members in particular and the community at large and participate in building the national economic order in order to create an advanced, just and prosperous society based on Pancasila and the 1945 Constitution. Cooperatives organize several types of businesses such as savings and loans, all-round shops there and general trade. In running the business, the cooperative conducts bookkeeping to find out the profit / loss of the cooperative and the amount of the remaining business results that are fair and comparable to the amount of business services of each member. In the bookkeeping process there were often several problems such as, not well recorded every small and routine transaction, there were miscalculation caused by human error, the slow process of calculating the remaining business results which caused delays in making the report. To overcome this, it is necessary to design a bookkeeping information system model in the form of use case diagrams as a design model and class diagram as a database model and system application as a form of implementation, so that it isable to create a bookkeeping information system for monitoring cooperative transactions in supporting the calculation of the remaining results business effectively, efficiently, quickly and accurately.
Companies that are engaged in general trading, suppliers and distributors for the procurement of goods or equipment and services to assist the operations and management of companies, especially improving company services in the field of drinking water are still using manual systems, where data is reported in written form, making it difficult , ineffective and efficient in inputting data and searching for goods. Therefore, this study proposes making a control inventory system to create a faster, more precise and accurate system. The analysis and modeling systems for programs use unified modeling language (UML) as a tool to help in object-oriented programming languages which are then implemented with the php programming language and use MySQL as a database. From this study, an inventory system can be produced that can monitor stock of goods in the warehouse properly, effectively and efficiently so that it can support services and improve customer satisfaction.
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