The Study of Tong Composter in Produced Liquid Organic Fertilizer

<strong>Abstract. </strong>Inadequate processing of waste in household areas has resulted in many people throwing the waste anywhere. Waste generation can be minimized by using the composting method. There are many methods of composting organic waste; one of them is the <em>Tong</em> Composter. <em>Tong</em> Composter is a composting method which in the process uses an EM4 bio-activator. <strong>Objectives:</strong> This research objective is to determine whether the liquid organic fertilizer parameters produced comply with the standard of Ministerial Decree of Agriculture No. 261 of 2019. <strong>Method and results:</strong> This research using the experimental method with two treatments, EM4 dosage (60 ml and 70 ml) and fermentation time (10 days and 20 days). A dose of EM4 in treatment 1 is 60 ml, and treatment 2 is 70 ml; each treatment was analyzed on the 10^th day. For treatment 3 and 4, a dose of EM4 in treatment 3 is 60 ml, and in treatment 4 is 70 ml; each treatment was analyzed on the 20^th day. Then from the liquid organic fertilizer produced, several parameters were analyzed at PT. Sucofindo Cibitung, such as Nitrogen, P₂O₅, and K₂O parameter. Meanwhile, the pH will be measured in the Environmental Engineering Laboratory of President University. The laboratory experiment results from pH, Nitrogen, P­₂O₅, and K₂O will be tested using the t-test. With the hypothesis (H_O: µ sample ≥ minimum value of macro-nutrient and pH parameter from Ministerial Decree of Agriculture of the Republic of Indonesia Number 261 of 2019). According to the t-test result, the null hypothesis is rejected because Nitrogen, P₂O₅, and K₂O in all parameters are less than 2%. The null hypothesis is accepted for pH because in all parameters is higher than or equal to 4. <strong>Conclusion:</strong> The t-test result showed that only the pH was complied with the standard, while the Nitrogen, P­₂O₅, and K₂O parameters have not complied with the standard of Ministerial Decree of Agriculture No. 261 of 2019.

LARGE-COPIOUS MINIPIGS OF ICG SB RAS: POTENTIAL OF UNREALIZABLE OPPORTUNITIES

This publication presents the results of the analysis of the dynamics of changes in the largefruited indicators of the breeding group of minipigs of the ICG SB RAS. The analysis showed that the four large-copious indicators are divided into two pairs. The first pair is made up of sample values of the characteristic: average and maximum. These indicators are characterized by stability throughout the studied period. The second pair includes the sample minimum values and standard deviations of the trait. These two indicators are dynamic: the sample minimum values are characterized by a decrease, and the sample standard deviations are characterized by a uniform increase, described by linear regression equations. It is shown that the dynamic characteristics are related to each other. It is determined that in this complex, the leader is the minimum value, and the follower is the standard deviation. This is explained by the fact that an increase in the standard deviation is associated with a decrease in the minimum value and the stability of the maximum in the studied period of time. The result of this process is the growth of the genetic potential in the breeding group, which is responsible for the high weight of the newborn individual. However, due to the small size of sows in comparison with commercial breeds (60-70 kg), this potential cannot be realized. Nevertheless, its redundancy ensures the stabilization of the maximum and average values of the trait - the mass of a newborn individual in minipigs of the ICG SB RAS. A possible way to increase the realization of the potential of large-copious breeding group is to reduce the multiple fertility of sows, which is quite solvable, but hardly advisable. Thus there is natural selection directed against individuals with a low birth weight in the herd. Natural and artificial selection for live weight of piglets at birth of 700 g or more, both help to stabilize the average value of the trait at the level optimal for the broodstock.

An Enhanced Classification Based Outlier Detection using Decision Tree for Multi class in Data Stream

Data Stream has continual, unbound, large and unstable records. The processing in data streams involves extracting significantidea in primary data of the kind static and dynamic with one sweep. In streaming, records are generated by thousands of data sources continuously and simultaneously. These data normally won’t have common range. Some data will be deviated from the rest in terms of variant factors. These are considered as outliers and it is tough to find those in data stream as they have multi dimensionality. Outliers, being the most abnormal observations, may include the sample maximum or sample minimum. E-commerce is an application or category of data stream that is generated from millions of sources at a time. It includes multiple products and transactions. Some products are cancelled during transactions and some are infrequent and these are termed as outliers. This paper focus on the challenge in E-Commerce and the objective is to aid the agencies in taking fine decisions in right time by finding the outliers using supervised learning scheme. This work is carried out in two phases. In first phase the outliers are detected and classified as cancelled and delivered products. In the second phase the least transaction is found as an outlier by the enhanced methodology for multi-class classification. The work is implemented in WEKA 3.9.6 and is compared with the existing works with evaluation metrics.

OPTIMAL MOMENT INEQUALITIES FOR ORDER STATISTICS FROM NONNEGATIVE RANDOM VARIABLES

We obtain the best possible upper bounds for the moments of a single-order statistic from independent, nonnegative random variables, in terms of the population mean. The main result covers the independent identically distributed case. Furthermore, the case of the sample minimum for merely independent (not necessarily identically distributed) random variables is treated in detail.

Covariance Prediction in Large Portfolio Allocation

Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. These matrices are used as inputs to obtain out-of-sample minimum variance portfolios based on stocks belonging to the S&P500 index from 2000 to 2017 and sub-periods. The analysis is done through several metrics, including standard deviation, turnover, net average return, information ratio and Sortino’s ratio. We find that no method is the best in all scenarios and the performance depends on the criterion, the period of analysis and the rebalancing strategy.