A field experiment was conducted at M/s.Rashtriya Chemicals and Fertilizers, Ltd., Mumbai, India, (RCF) experimental farm to evaluate the effect of ZnO Nanoparticles (ZnO NP) in combination with N: P: K (15: 15:15) complex fertilizer “Suphala” of RCF Ltd. on growth attributes of brinjal (Solanum melongena L) as well as nutrient use efficiency. The experiment was carried out in randomised block design with three replications. The first treatment (T-1), comprised of recommended dose of fertilizer (RDF), N: P: K (50:50:50), applied at the time of transplantation. The second treatment (T-2) was conducted with RDF in combination @ 2kg ZnSO4 (bulk)/ha. The third treatment (T-3) was added, N: P: K (12.5; 12.5; 12.5) in combination to ZnO NP @ 4500mg/ha. The forth treatment (T-C) was without any fertilizer. All treatments were given appropriate quantity of nitrogen per hectare as urea at the 30th day of transplantation. The combination N: P: K (12.5; 12.5; 12.5) and ZnO NP @ 4500mg/ha yielded 91% and 45.3% higher brinjal yield and biomass respectively than the treatment with only RDF. It was also observed that 38% and 21% higher yield and biomass respectively were recorded in the treatment where combination of RDF with ZnSO4 (bulk) over RDF was used alone. The results of field trials reveal that, there was synergistic effect of ZnO NP @ 4500mg per hectare with N: P: K complex fertilizer on growth attributes of brinjal as well as nutrient use efficiency.
The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.
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