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.
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:Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Design of mufflers is a complex function that affects back pressure, noise characteristics, emission and fuel efficiency of engine. Back pressure caused by exhaust system has negative effect on engine efficiency, resulting in decrease of power. Muffler to be design in such a way that the back pressure should be optimum and it should not disturb the subsystem operations. Hence, goal of this paper was to shorten product development cycle time through back pressure optimization. There are number of formulas for back pressure and each formula gives different result. So, to minimize the number of iteration to finalize any one of the back pressure formula, CFD analysis was performed. For validation of the back pressure theoritical formula and CFD result, simple pipe flow calculation and CFD analysis has been performed, so that at least maximum error percentage can be predict. By comparision of all three formula results with CFD analysis and experiment value have been tabulated to find error percentage. The target of the project work was to get error % of CFD and Theoretical formula to get below 15%. The minimum and the best result came for formula II that is taken up by benchmarking company’s installation guide of exhaust system for which error % was 14%. This paper gives prediction of back pressure value during its preliminary stage of design.
A field experiment was carried out at Regional Research Station, Bawal (Rewari), CCS Haryana Agricultural University, Hisar, Haryana (India) during kharif season 2018 to study the effect of mechanized weed management in rainfed cluster bean (Cyamopsis tetragonoloba L.). The major weeds in the field were Cyperus rotundus, Digera arvensis, Trianthema portulacastrum and Eragrostis spp. The weed density and dry matter accumulation of weeds was significantly reduced by mechanized weed control methods than manual. Higher weed control efficiency was recorded under T6 (94.1%) and T5 (90.9) over T1 (16.0 %) and T2 (2.45 %) respectively. Two simultaneous mechanized interculture (at 20 and 35 DAS) with power weeder (T6) and tractor drawn cultivator (T5) under row spacing of 60 cm were found superior to recommended manual weeding once either with kasola (T1) or wheel hand hoe (T2) performed at 27 DAS under 45 cm row spacing.
Massive data are generated by social media users including posts, tweets, images, and videos. Getting valuable information from this big data is a significant, challenging, and interesting issue in the text mining domain. Twitter data are analyzed with text mining techniques to discover society agenda, trends, user behaviors, and feelings. Text analysis method to determine sentiments from tweets is proposed in the present research. Apache Flume is used to collect data stream from Twitter and store into Apache Hadoop. Natural language processing techniques are carried out to put the data into meaningful context followed by a classification model training with data mining methods. It carries out the classification label as people’s opinion, such as positive, negative, and neutral sentiments, using Twitters streaming data. 10 different automobile brands are selected and collected tweets with hashtags about these brands by using Apache Flume are used as case study. Collected data have been pre-processed using TF-IDF, Bi-gram, and SVD metrics and a classification tree model has been generated and the results are compared. The results that were experimented indicated that the classification tree based on SVD has the best accuracy. According to the different brands model, based on bigram is the most stable and performs with the best accuracy. The results from the experiments indicate that the model that uses Bi gram could be used to address data with complex behavior in the sentiment detection.
Research on video description and human activity recognition has been dramatically improving study finding on visual monitoring. Monitoring activities in the examination is a yet unsolved problem in which the students can perform various activities in an Exam room. Such activities can be monitored automatically through an automated surveillance system. We use squeeze net and VGG16 as deep learning constructs for deep feature extraction. These features are then fused serially to form a single feature set. The entropy and ant colony optimization (ACO) based feature selection approaches are applied separately on the acquire feature subsets having qualities of both filter and wrapper-based approaches. The separately selected features are then ensemble to obtain a powerful features subset. SVM based classifiers are finally applied for prediction. From the Exam activities detection dataset, the classification algorithm precisely labels the student activities into abnormal and normal classes. The results depict that the suggested framework for activity recognition in Exam is very effective with acceptable accuracy. The framework will help to analyze student activity in exams, to improve the examination system.
Innovation is seen as one of the effective levers for businesses in the face of competition and rapidly changing markets. However, if technological innovation is identified in its practices and objectives, organizational or managerial innovation is still the subject of controversy and divergence as to its scope of action and research. Today, none, if not the majority of Moroccan industrial companies, especially those operating in the textile sector, have not been able to deploy a managerial transformation despite the emergence of different departments. This document presents advice to the leadership of organizations and other managerial practices inspired by Lean Management, Lewin's theory, and the Kotter model allowing the managerial and agile transformation of industrial companies.