PENDUGAAN PERKEMBANGAN POPULASI ORYCTES RHINOCEROS L. (COLEOPTERA: SCARABAEIDAE) DI PERKEBUNAN KELAPA SAWIT (ELAEIS GUINEENSIS JACQ.)

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Yulia Pujiastuti., dkk
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Environmental Factors ◽  
Rainfall Intensity ◽  
Elaeis Guineensis ◽  
Natural Control ◽  
Beetle Population ◽  
Oryctes Rhinoceros ◽  
Forecasting Method ◽  
The Many

Approximately 1.000 species of insects are associated with coconut worldwide. Over 40 species of coleopteran pests have been recorded – most are under effective natural control but some require interventions. In view of the increasing and devastating damage by coconut beetle (Oryctes rhinoceros) to coconut palms in the many countries, many efforts are made to find appropriate method to forecast its population. The basic procedures of these monitoring programs are outlined together with forecasting method. A study to forecast coconut beetle population has been carried out in palm oil estate, near Palembang. Study was aimed to estimate population after several observations of beetle population. Another aim was to assess the influences of environmental factors, i.e. temperature, relative humidity and rainfall intensity, which could affect to the beetle population fluctuation. Results showed beetle population could 65.12 percent in average due to unsuitable environmental factors. At first observation temperature was recorded 26 – 30 0C and 1185 larvae werefound. At last observation larvae population decreased to 392 larvae at temperature 29 – 33 0C. It was found that relative humidity and rainfall intensity also played important role in decreasing beetle population.Keywords: coconut beetle, time series analysis, population estimation, environmentalfactors

scholarly journals PERAMALAN PERSEDIAAN INFUS MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA RUMAH SAKIT UMUM PUSAT SANGLAH

2018 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
I PUTU YUDI PRABHADIKA ◽  
NI KETUT TARI TASTRAWATI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Time Series ◽  
General Hospital ◽  
Moving Average ◽  
Glucose Infusion ◽  
Series Method ◽  
Autoregressive Integrated Moving Average ◽  
Important Thing ◽  
Forecasting Method ◽  
Time Series Method ◽  
The Many

Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indicate the need for infusion at Sanglah Hospital as many as 154,831 units for infusion of 0.9% NaCl 500 ml and 8,249 units for 5% 500 ml Glucose infusion.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Exposure Assessment of Airborne Bacteria Emitted from Swine Manure Composting Plant

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1283
Author(s):  
Ki-Youn Kim
Keyword(s):  
Relative Humidity ◽  
Environmental Factors ◽  
Swine Manure ◽  
Airborne Bacteria ◽  
Particulate Matters ◽  
Distribution Characteristics ◽  
E Coli ◽  
Enterococcus Spp ◽  
Staphylococcus Spp ◽  
Rotary Type

This study was performed to investigate the distribution characteristics of airborne bacteria emitted from swine manure composting plants. The types of swine manure composting plants selected for the survey in this study were as follows: screw type, rotary type, and natural dry type. Mean levels of airborne bacteria in swine manure composting plants were 7428 (±1024) CFU m−3 for the screw type, 3246 (±1407) CFU m−3 for the rotary type, and 5232 (±1217) CFU m−3 for the natural dry type, respectively. Based on the results obtained from this study, the swine manure composting plant operated by screw type showed the highest concentration of airborne bacteria, followed by the natural dry type and rotary type. The monthly concentration of airborne bacteria was the highest in August and the lowest in November, regardless of the type of swine manure composting plant. The respirable size of airborne bacteria accounted for about 50% of the total. The ratio of respirable to the total quantity of airborne bacteria was 50%. The correlation relationships between airborne bacteria and environmental factors (temperature, relative humidity, particulate matters, and odor) were not found to be significant in the swine manure composting plants. The predominant genera of airborne bacteria identified were Micrococcus spp., Staphylococcus spp., Escherichia(E-coli) spp., Enterococcus spp., and Enterobacteriaceae spp.

Algorithms to predict moisture content of grain using relative humidity time-series

2020 ◽  
Author(s):  
Charles B. Delahunt ◽  
Wenbo Wang ◽  
Simon Ghionea ◽  
Andrew Miller ◽  
Austin Chan ◽  
...  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Moisture Content

EFFECTS OF ENVIRONMENT ON GROWING HYDROPONICS MAIZE FODDER USING LOW-COST GREENHOUSE CULTIVATING UNIT FOR LIVESTOCK PRODUCTION.

2021 ◽  
pp. 71-72
Author(s):  
Adamu, B. ◽  
Abdullahi, S. ◽  
Saidu, S. G ◽  
Yustus Sunday Francis
Keyword(s):  
Relative Humidity ◽  
Environmental Factors ◽  
Production System ◽  
Low Cost ◽  
Factors Affecting ◽  
Production Water ◽  
Fodder Production ◽  
Food Requirement ◽  
Greenhouse Cultivation ◽  
Near Future

The term 'Hydroponics' was derived from Greek words 'hydro' means water and 'ponics' means labor. Hydroponic is a modern agricultural technique that uses nutrient solution rather than soil solution for fodder production. As population increases the food demand also increased, the existing system of agriculture will not be able to meet the food requirement in the near future due to environmental challenges in the industry. The major environmental factors affecting the hydroponics production system are; Temperature, relative humidity, and light. The objectives of this studies are to examine the hydroponics greenhouse technologies, impact of environmental factors on hydroponics greenhouse cultivation and challenges of growing on hydroponics greenhouse system. This study revealed that hydroponics greenhouse cultivation is a better option for improved fodder production, water utilization, palatability and digestibility.

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

scholarly journals Forecasting method for grouped time series with the use of k-means algorithm

2015 ◽  
Vol 9 ◽  
pp. 4813-4830 ◽  
Author(s):  
Nadezhda N. Astakhova ◽  
Liliya A. Demidova ◽  
Evgeny V. Nikulchev
Keyword(s):  
Time Series ◽  
Forecasting Method
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