AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering. Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology Kongzhi yu Juece/Control and Decision Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980)
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
A technical-economic analysis of municipal solid waste (MSW) gasification was carried out in the commune of Chillán, Chile. The MSW production was quantified and characterized in the 2015-2018 period. The percentage characterization of MSW corresponded to organic matter (61 %), other waste (17 %), plastic (10 %), paper and cardboard (8 %) and glass (4 %). In the analysis, the countercurrent fixed-bed gasification technology was selected, due to the simplicity of operation and less difficulty in controlling the operating parameters. Flow diagrams and gasification mass and energy balances were developed incorporating three preliminary processing options for the wet fraction of MSW: biodigestion, drying and pressing, prior to gasification. Total energy efficiencies were 54.2 %, 54.6 %, and 61.4 % respectively. Finally, a preliminary economic analysis was carried out considering income and costs for the three process alternatives. The approximate annual gross profits were estimated at 6,462,000 US $ ∙ year-1 for press-gasification, 6,139,000 US$ year-1 for drying-gasification and 4,600,000 US$ year-1 for biodigestion-gasification.
Canarium tree as a source of food, energy, health and cultural development has been widely discussed but not in terms of biomass content. In general, studies have not been carried out properly regarding its ability to provide environmental services, especially in dealing with climate change. Therefore, this study aims to determine the value of Biomass Expansion Factors (BEFs) and Root to Shoot ratio (R/S). Beside that, to determine the allometric equation for young Canarium to be used in dealing with climate change. This is done through the Monitoring Reporting and Verification (MRV) System under the United Nations Guidelines for Reducing Emissions from Deforestation and Degradation (UN REDD). There were 32 young canaries at seedling and sapling levels aged 9-21 months in the nursery area, with a diameter of 3.4 - 4.9 cm and a height of 1.32 - 2.48 m. Before determining the allometric equation, the classical assumption test is carried out first. Young canaries have R/S value 0.52 and BEF values 1.50. The allometric equation is Y1 = -700,200 + 209.149X1 + 3.922X2 where Y1 = biomass above ground level, X1 = diameter, X2 = height with R squared 0.386. To calculate the total above and below-ground biomass simultaneously, the following 3 steps were carried out: (1) Calculating above-ground biomass using the allometric equation Y1 = -700,200 + 209.149X1 + 3.922X2, (2) calculating below-ground biomass by utilizing R/S value 0.52 and (3) summing above and below-ground biomass.
Building a dynamic model to meet control requirements is a challenging problem. This paper presents a sliding mode control law and proves its stability for parallel robots containing parameter uncertainties and noise. Next, simple and complex dynamic models were built to establish the sliding mode control law. Finally, trajectory errors of the control using two different dynamic models are compared to conclude which model is more suitable.
Variable rate application is an effective way to realize low-pollution and high-efficiency use of pesticides. Real-time and accurate identification of disease information is a key prerequisite that affects the development of variable application technology. At present, the identification of diseases is mainly determined by the method of artificial field sampling, which not only is time-consuming and laborious, but also has disadvantages such as poor representativeness, strong subjectivity, and poor timeliness. Rice blast, which is the most severely affected by rice, is chosen as the object in this paper. Firstly, this paper proposes a rice blast online monitoring and real-time spraying system suitable for rice field sprayer. The pesticide will be sprayed according to the severity of the disease during the sprayer doing field inspections. Secondly, existing convolutional neural networks have slow convergence speeds in the identification of small samples of rice blast, which is prone to problems such as over-fitting. To solve the meaning problem, this paper proposes a rice blast monitoring algorithm based on migration learning. The knowledge learned by the VGG-16 network on the ImageNet image data set is transferred to this model, and a brand-new fully connected layer is designed. Finally, experiments show that the accuracy of the constructed rice blast identification model can reach 97.18%. It provides a reference for the intelligent diagnosis of diseases.
The difficulty of harvesting safflower filaments is a problem for the development of the safflower industry. In this research, a harvester that uses reciprocating shearing to harvest safflower filaments was developed and then tested by harvesting No. 5 Yumin stingless safflower plants in Xinjiang. Whether to install fixed blades, the DC motor speed, and the installation angle were the test factors, and the net harvest rate and impurity content were the response indices. The optimal parameter combination was obtained by a quadratic orthogonal regression test. The optimal parameters were DC motor speed of 150 r/min, installation angle of 45° and fixed blades. In field testing, the harvesting rate of safflower filaments was greater than 95%. The test results were close to the predicted values, and the model was verified as effective.