bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stability analysis of a predator-prey model with stage structure
Authors: Pang Qiyue , Gao Yang 
Published: June 2022

Abstract
Many scholars have explored the population dynamics system, functional response, time-delay response, and so on. Based on the existing research, this paper studies a class of predator-prey models with stage structure, time delay, and Holling type-II functional response function. The stability of that model at the positive equilibrium point, the sufficient condition for stability, and the existence of Hopf bifurcation are discus. The model was numerically simulated by taking appropriate parameters and different time delay values, variation diagram of each component, and solution curves were given near the critical value. The results show that the stability of the system will change with the variation of the bifurcation parameter value, and Hopf bifurcation will occur.
A Novel Condenser Vacuum Degree Prediction Model Based on LSTM and MemN2N
Authors: Sun Ziwen , Wang Tao , Lu Yanning , Chen Bo , Jin Yawei , Guan Shipian , Si Jiasheng 
Published: June 2022

Abstract
Condenser vacuum degree prediction of power plants is a challenge task in power system security field. Most existing studies are based on shallow machine learning algorithms, which fail to leverage historical data comprehensively, resulting inaccuracy and unreliable predictions. Therefore, using a serialization model like Recurrent Neural Network to capture time-series information from historical data is necessary. However, these serialization model alone has inherent defects in dealing with long-distance dependence, which may cause historical information forgetting problem. This paper proposes a new prediction model combining LSTM and End-To-End Memory Network (MemN2N). We use LSTM to mine the long-distance dependency information in historical data, and introduce the encoding historical information into the memory pool of MemN2N. MemN2N allows better preservation of historical information for serialization model LSTM, and can make accurate and reliable predictions through soft attention mechanism. Through the experiments on real data from the power plant show that, compared with other prediction models, the model proposed in this paper achieves higher prediction accuracy and has great engineering value.
Condenser Vacuum Degree Prediction Model with Multi-View Information Fusion
Authors: He Pengfei , Zhu Yingjie , Chen Bo , Lu Yanning , Yao Yongling , Xiao Jie , Si Jiasheng 
Published: June 2022

Abstract
Vacuum degree is a crucial factor for the operation of thermoelectric generating set. Existing approaches typically use machine learning algorithms to link the relationship between unit operating data and condenser vacuum degree by focusing on the temporal information within the data, while ignoring the frequency information implied in the historical condenser vacuum degree. To fully use of frequency information and further improve prediction accuracy, we propose a novel condenser vacuum degree prediction model with multi-view information fusion. In specific, the implicit frequency information in the historical vacuum degree sequence is explored via a combination of Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN). Furthermore, Transformer encoder is used to extract the temporal information from the unit operating data. Finally, the two views information are fused for condenser vacuum degree prediction. Extensive experiments conducted on the real data collected from a power plant demonstrate the superiority of the proposed method over several state-of-the-art methods.
Application of LSTM-LightGBM Nonlinear Combined Model to Power Load Forecasting
Authors: Zhou You , Lin Qing , Xiao Di 
Published: June 2022

Abstract
The accurate prediction of power system load is extremely important for the operation of the power market and the safe operation of the power grid. In order to improve the accuracy of short-term load forecasting of power systems, a combination model based on long short term memory network (LSTM) and light gradient boosting machine (LightGBM) is proposed. The experiment first decomposes historical load data by EMD, uses historical weather data and load data decomposed by EMD to establish LSTM prediction model and LightGBM prediction model respectively, and then these two predicted values are linearly combined to obtain the final predicted value. The electrical load data of the 2016 Electrician Mathematical Contest in Modeling is used as an example to verify. The experimental results show that the LSTM-LightGBM combined model has higher forecasting accuracy and application prospects for power load forecasting than traditional load forecasting methods and standard LSTM and LightGBM load forecasting methods.
Design and Implementation of Groundwater Monitor Based on GPRS Transmitted
Authors: Kuang Xingchen , Wang Laiwei , Yu Wenjing , Liu Xianchun 
Published: June 2022

Abstract
In practical applications, the geological environmental monitoring instruments should have more stringent instrument properties, their resistance to high and low temperature, sealing, corrosion resistance and low power consumption, They are all have higher requirements. While the areas whitout public network coverage are mostly alpine high - altitude areas, the instruments are exposed to relatively harsh geological conditions for a long time. The timely and stable data communication transmission technology is the key technology to ensure stable operation of groundwater dynamic monitoring systems. At present, GSM short message format is mostly used in the groundwater dynamic remote monitoring technology, but this approach is extremely limited in field applications. For obtaining groundwater monitoring parameters not affected by the limitation of public wireless network coverage area, the GPRS satellite positioning and short message technology is adopted, and a kind of data transmission communication apparatus using GPRS satellite is researched. After a large number of indoor debugging and field stability tests, it is proved that the control terminal of groundwater dynamic monitoring and communication developed based on GPRS satellite communication features stable data transmission, low power consumption, good resistance of wind, sand and moisture. It solves the transmission obstacle of groundwater monitoring data in the area without mobile phone signal, and realizes multi - mode groundwater monitoring communication.
The Effect of Plant Densities in System of Rice Intensification (SRI) Method to Water Productivity of Paddy Field in East Sumba, East Nusa Tenggara
Authors: Nugroho B D A , Arif C , Suryandika F , Hapsari U , Nihayah B , Muslihin  
Published: June 2022

Abstract
Principle of System of Rice Intensification (SRI) adapted to local conditions to increase productivity and environmentally friendly. The main component of SRI is single seedling per hill, young seedlings, wider spacing, intermittent irrigation, inter-cultivation, and organic fertilization. This study area of the System of Rice Intensification is in East Sumba Regency, East Nusa Tenggara, which has typical areas like soil texture, bulk density, soil water content, and rainfall. The research aimed to know the effect of plant densities in the System of Rice Intensification (SRI) method on the water productivity of paddy fields. This research initially focused on transplanting very young rice seedlings of 14 days old in two treatments of plants. The densities are broad in a square pattern (30 x 30 cm) and a Jajar Legowo pattern (20 x 10 x 40 cm). The rice was not grown in flooded paddies but moist ground, with intermittent irrigation. When SRI was concerned at a different wider spacing, it discovered the effect on water productivity for paddy fields, especially in East Sumba, East Nusa Tenggara. So that it determined the best wider spacing of the System of Rice Intensification method can apply in the area. The results show that the Jajar Legowo pattern spacing (20 x 10 x 40 cm) has a higher water productivity value of 3,4% than the spacing treatment (30 x 30 cm). Jajar Legowo was 11.9% higher than the spacing treatment (30 x 30 cm).
Hydraulic Performance Evaluation of Low-Cost Gravity-Fed Drip Irrigation Systems Under Constant Head Conditions
Authors: Martinez C G , Wu C L R , Fajardo A L , Ella V B 
Published: June 2022

Abstract
The use of drip irrigation for upland crop production has gained popularity in developing countries due to its relatively high water-use efficiency. To maximize the efficiency of this irrigation method, it is important to evaluate its hydraulic performance in terms of emitter discharge and water distribution uniformity under various operating heads. In this study, the hydraulic performance of two locally available low-cost drip irrigation kits -referred to in this study as Drip Kit A and Drip Kit B - was assessed and compared under constant head conditions. Both drip kits are composed of a 1000-L intermediate bulk container (IBC) tank, 10-m submain line, 20-m lateral lines spaced 0.75 m apart, and cascade labyrinth emitters spaced 0.30 m apart along the laterals. Operating heads of 2.5, 3.5, 4.0, 4.5, 5.0, 5.5 m were used and maintained throughout the trials with the use of an overflowing tank. The submain and lateral lines were levelled (0% slope). Sampling of discharge rates was done for one-third of the total number of emitters for each of the tests performed. Results showed that the overall system emitter discharge rate generally increases with increasing operating head. Moreover, emitters farther from the submain line generated relatively lower discharge rates than those closer to it. Analysis of the experimental data showed that the Christiansen’s coefficient of uniformity (CU), emission uniformity (EU), and coefficient of variation (CV) ranged from 97.5 to 98.5, 95.9 to 97.7, and 0.02 to 0.04 for the drip systems tested. Further statistical analysis also showed that varying the operating heads does not have a statistically significant effect (α=5%) on the CU, EU, and CV of the system. Based on the results, Drip Kit B performed better than Drip Kit A in terms of water distribution uniformity. However, the latter could generate higher emitter discharge rates than the former. For both drip kits, an operating head of 2.5-m is recommendable for a 200 m2 plot, from the practical standpoint. This study has generated important empirical data that could serve as a basis for maximizing the performance efficiency of locally available drip irrigation systems for a more efficient irrigation water management.
The Effect of Biofertilizer from Waste Bioconversion on The Growth of Cocoa Seedlings
Authors: Sasmita K D , Rokmah D N , Sakiroh  , Hafif B , Putra S 
Published: June 2022

Abstract
Organic waste can be a source of environmental pollution if not managed properly. The organic waste transformed by Black Soldier Fly (BSF) larvae produces liquid biofertilizer (LB) containing nutrients and beneficial microbes for plants. This study aims to determine the effect of dosage and frequency of application of liquid biofertilizer on the growth of cocoa seedlings and soil properties. The experiment used a randomized complete block design with seven treatments and four replications. The treatments were a control, LB 3% every two weeks, LB 3% every four weeks, LB 6% every two weeks, LB 6% every four weeks, LB 12% every two weeks, LB 12% every four weeks. The results showed that LB 6% every four weeks was the best treatment in increasing seedling height, stem diameter, and weight of fresh shoot, dry shoot, and dry roots of cocoa seedlings at 20 weeks old compared to other treatment. The application of LB significantly increased soil pH, but not significantly affected on the content of organic C, total N, available P and exchangeable cations at soil. Biofertilizer from bioconversion of organic waste using BSF larvae is suitable to improve the growth of cocoa seedlings.
Techno-economy Analysis of Shallot Seedling Production form TSS (True Shallot Seed) with LCAC (Low Cost Aeroponic Chamber) Technology
Authors: Solahudin M , Sucahyo L , Amarilis S , Purnamasasi L A 
Published: June 2022

Abstract
The decline in the productivity of shallots occurred in almost all areas of shallot production centres in Indonesia. One of the factors in the decline in productivity is that farmers are still dependent on tuber seeds that are produced by themselves from generation to generation without any risk of carrying degenerative diseases from previous shallots. Another problem is that the cost of providing seeds is quite high, reaching 40% of the total production cost, with an average requirement of 1-1.5 tons/hectare. Planting shallots using TSS (True Shallot Seed) is an alternative solution, in terms of seed requirements, only 3-5 kg/ha is needed. However, an initial activity is needed in the form of the production of shallot planting material before it is transferred to the land. This study aims to conduct a technoeconomic analysis of shallot planting material production from TSS with LCAC (Low Cost Aeroponic Chamber) technology. Techno-economic analysis is carried out by calculating the basic costs for producing shallot seeds, and economic analysis related to NPV, IRR, Net BC Ratio and Payback Period. This study uses a scenario design of production scales of 200, 500, 1000 trays, and 1 million seeds on a production schedule of 3 cycles per year and 6 cycles per year. The NPV calculation yields profits ranging from 4,750,650 - 822,448,953 IDR, IRR 9.87 - 65.40% above the discount rate used of 6%. Net B/C ratio 1.12 - 2.24 and initial investment will return to investors in the range of 5.23 to 1.28 years after production starts.
Hydropic Rice Nursery for Rice Transplanting Using Rice Transplanters
Authors: Astika I W , Hartono F F 
Published: June 2022

Abstract
Rice nursery using dapogs requires a series of media preparation activities involving sieving the soil, mixing with organic fertilizer and rice husk charcoal, which are troublesome for the farmers. Another practical method is to grow the rice seedlings in an hydroponic system. The purpose of this research was to develop a hydroponic system to grow rice seedlings until ready for transplanting with rice tranplanters and then to transplant the seedlings with a transplanter. The seedling nursery were designed having 10 level rack with 12 trays on each level. It is designed DFT, NFT and flooding hydroponic systems. Finally flooding system, nutrient concentration rate of 2.4 mS and seed density 5 seeds/cm2 were used in the research as those treatments gave the best results. Light illumination at rack level 1-6 were significantly different from level 7 (the top layer) and consequently level 1-6 suffered from etiolation problems making the seedlings less vigor. Transplanting results showed a wide range of number of clump/hill both at “lees” and “middle” machine setting. Root zone without any soil media made a high friction force when the seedling mat sliding down to the planting fork. A slight media at the bottom of tray might be required as the slippering media.
The effect of temperature distillation on products distribution derived from wood pyrolysis bio-oil
Authors: Irawansyah Herry , Amrullah Apip , Fitrah Jayadi 
Published: June 2022

Abstract
Bio-oil from biomass pyrolysis can be converted to chemicals, solid carbon materials, and syngas by various thermochemical conversion technology methods. Because of the elevated temperature, bio-oil suffers drastic component changes as the first step in these processes. Understanding the impact of heating temperature on bio-oil transformation during the distillation process is critical for practical bio-oil usage. A bio-oil feedstock produced from the pyrolysis of lamtoro wood residue at 500 °C was used in distillation from this work. Complete temperature range analysis of 96, 97, 98, 99, and 100 °C was conducted. Eight typical compounds in bio-oil were precisely quantified by gas chromatography-mass spectroscopy (GC-MS) and FTIR analysis. The results revealed that a common critical point was widely present during the distillation process, which may be attributed to a stable system generated by hydrogen bonding. As a result, the content of acetic acid, 2-Propanone, and phenol was up to 38%, 12%, and 20%, respectively. The detailed distillation characteristics and product distribution provide a comprehensive insight into the reaction process and component enrichment patterns, assisting design and parameter optimization.
Oil Palm Productivity Estimation Based on Landsat 8 Vegetation Index, Rainfall, and Fertilization Dosage at PT PSJ, South Sumatera
Authors: Savitrie N P , Liyantono  
Published: June 2022

Abstract
Estimating oil palm production is one of the essential factors in palm oil management planning which is still done conventionally. This process uses more workers to take data samples from the fields that need more energy and time to get the data. Meanwhile, the yield estimating process could be done using a remote sensing method to save more energy and time on its implementation. This research was conducted to determine the estimation model of oil palm productivity using parameters from the remote sensing method and the company’s data, and determine the relationship between current productivity and the previous data. The parameters used to estimate palm oil productivity are NDVI from Landsat 8, rainfall, rainy days, fertilization dosage, and age of the palm oil tree from the company’s data. These parameters were shifted several other months back (M-1, M-2, M-3, M-6, and M-12) and grouped into 6-Months and 12-Months to estimate the palm oil production. The result showed that using the previous data can estimate oil palm productivity. The data that can be considered to estimate palm oil production is 12 months (M-12) shifted back. Also, using 3 parameters (NDVI value, rainfall, and fertilization dosage) is enough to estimate palm oil production.
Classification of Tomato Disease using Convolutional Neural Network
Authors: Hamami Faqih , Dahlan Iqbal Ahmad 
Published: June 2022

Abstract
Agriculture is an important sector for national food needs. The main crops in agriculture include rice, wheat, potatoes, sugar cane. Other crops include nuts, fruit, vegetables, tubers and others. Plant diseases are one of the problems that always arise in the agricultural sector. Every food ingredient has the potential to have disease. Usually diseased plants will be given pesticides. Detection of plant diseases is very important. Especially if we know the type of disease from the plant. To find out the type of disease with the naked eye is quite difficult, especially since the form of the disease has a similar pattern. In this research, we propose to classify the tomato disease from its leaf with thousands of tomato images. There are three types of analysed diseases they are bacterial spot, early blight and yellow leaf curl. We implement convolutional neural network approach to find the best classifier model. From various experiments, it was found that the neural network architecture that was built could achieve accuracy up to 87%
Performance evaluation of a water level sensor under various turbidity levels in lowland crop production systems
Authors: Pereira G S , Ramirez R C , Agulto E S , Ella V B 
Published: June 2022

Abstract
The practice of alternate wetting and drying (AWD), a water-saving technology in lowland crop production systems, can be greatly facilitated using wireless water level sensors. However, these sensors generally work under clear water conditions. The sensitivity of these sensors to turbidity is important for accurate water level measurement and appropriate irrigation scheduling. This study evaluated the performance of a high-end water level sensor of the submersible pressure transducer type under various turbidity levels. The performance tests were performed in the laboratory using water samples collected from a typical lowland rice production system under various levels of turbidity replicated three times with clear water as control. The readings of the sensors were compared with manual readings for each turbidity level in all replications. Results showed that the measured water level depth generally increases with increasing turbidity for each voltage level. The linear regression or calibration equation developed for each turbidity level proved to be adequate as evidenced by a relatively low RMSE of less than 1 mV. Results of ANOVA suggest that turbidity significantly affects the accuracy of the water level sensor (p <.001). A unified calibration equation (R2=0.9985 and RMSE=1.971 mV) was developed to account for the effect of turbidity up to 4300 FAU on the water level measurements. Results of this study can be used to improve the accuracy of water level monitoring in irrigated lowland crop production systems employing alternate wetting and drying technology to further increase irrigation efficiencies and augment water savings particularly during the dry season or under water-scarce conditions for a more sustainable crop production.
Conventional and sensor-based streamflow data acquisition system for sustainable water resources management and agricultural applications: an extensive review of literature
Authors: Llaban A B , Ella V B 
Published: June 2022

Abstract
This paper presents an extensive review of literature of conventional and sensor-based methods for streamflow data collection intended for sustainable water resources management and agricultural applications. Conventional methods reviewed are direct measurements, e.g., floating method, volumetric method, current meter, trajectory, dilution method, Acoustic Doppler current profiler and electromagnetic. Hydraulic structure methods are also included, and remote sensing and particle image velocimetry as non-contact approaches. This paper also presents a review of published literature on local studies in the Philippines that employed sensor-based measurement of streamflow for water resources management. A comparative analysis of the various methods was then made based on their applicability with respect to the characteristics of the streams, ease of operation, effectiveness, accuracy, and the cost. Results showed that for most cases, when resources are available, current meter method is still, the best method to be used due to its high accuracy, time efficiency and versatility. For hilly terrain which covers larger areas, Particle Image Velocimetry (PIV) is suitable with accurate results. For long term measurements, hydraulic structures maybe considered due to its high accuracy when installed properly and if the conditions are met. Additionally, when other methods are impractical and impossible to use, float method is best due to its advantage in operation and cost especially in flat and unobstructed terrain. For channels with small quantity of streamflow that converges into a single descend like a waterfall, volumetric method is suitable. Sensor-based methods are the most effective for real-time monitoring of streamflow and they are applicable to almost any terrain and river size. In selecting the most suitable method for streamflow monitoring, the characteristics of the river or stream, ease of access, accuracy, and availability of financial and physical resources must be considered. Finally, recommendations are offered to maximize the potential applicability of the most effective methods for sustainable water resources management and agricultural applications particularly in developing countries.