Application of Analytic Hierarchy Process in Engineering Education

Analytic Hierarchy Process (AHP) provides a mathematical technique to formulate a problem as a hierarchical structure and believes in an amalgamation of quantitative and qualitative criteria. It is this uniqueness of AHP that makes it one of the important inclusive systems, considered to make decisions with multiple criteria. This paper focuses on conducting Analytic Hierarchy Process, based on the data collected from several Engineering colleges in the state of Telangana. This paper aims to understand the reasons for removing the staple Engineering streams such as Mechanical engineering, Production engineering, Electronics and Instrumentation engineering and introducing new and contemporary streams such as Artificial Intelligence and Data Science, Artificial Intelligence and Machine Learning and Internet of Things. The World Economic Forum’s latest “Future of Jobs” report highlights the impact of ‘double disruption’ of Automation, followed by COVID-19. The report indicates that while 85 million jobs will be displaced, 47% of core skills will change by 2025. The topic thus is of immense value since it looks closely at the paradigm shift mentioned above and its further consequences. The result of the present study would be helpful to indicate the exact rankings of the programming and non-programming branches in the engineering field and thus would be instrumental in gauging learners’ inclination towards studying specific branches. This paper aims to analyze the growing demand of programming branches over traditional, non-programming branches.

Structural, magnetic and gas sensing properties of nanosized copper ferrite powder synthesized by sol gel combustion technique

Stoichiometric nano sized copper ferrite particles were synthesized by sol gel combustion technique. They were then calcined at various temperatures ranging from 300–800°C and were either furnace cooled or quenched in liquid nitrogen. A high magnetisation value of 48.2emu/g signifying the cubic phase of copper ferrite, was obtained for sample quenched to liquid nitrogen temperature from 800°C. The ethanol sensing response of the samples was studied and a maximum of 86% response was obtained for 500ppm ethanol in the case of a furnace cooled sample calcined at 800°C. The chemical sensing is seen to be correlated with the c/a ratio and is best in the case of tetragonal copper ferrite

Inheritance of juvenile traits and immune competence in Gramapriya male line chicken

High correlation estimates between body weight and shank length revealed strong association among traits. The significant positive association between 4 and 6 weeks shank length and body weight in the Gramapriya male line chicken suggest the breeder to pre-pone the selection to 4 weeks age which economizes the production cost. Besides, the immune competence of the birds was also found to be better, making them suitable male line for production of backyard poultry varieties

SRI-A Method for Sustainable Intensification of Rice Production with Enhanced Water Productivity

Climate change induced higher temperatures will increase crops’ water requirements. Every 10°C increase in mean temperature, results in 7% decline in the yield of rice crop. Hence, there is a need to develop water saving technologies in rice which consumes more than 50% of the total irrigation water in agriculture. System of Rice Intensification (SRI) is one such water saving rice production technology. Experiments were conducted at different locations in India including research farm of Directorate of Rice Research (DRR), Hyderabad, during 2005-10 to assess the potential of SRI in comparison to normal transplanting/Standard Planting (NTP/SP) under flooded condition. SRI recorded higher grain yield (6 to 65% over NTP) at majority of locations. Long term studies clearly indicated that grain yield was significantly higher (12-23% and 4-35% over NTP in Kharif and Rabi seasons, respectively) in SRI (with organic+inorganic fertilizers) while the SRI (with100% organic manures), recorded higher yield (4-34%) over NTP only in the Rabi seasons. Even though, SRI resulted in higher productivity, the available nutrient status in soil was marginally higher (10, 42 and 13% over NTP for N, P and K, respectively) at the end of four seasons. There was a reduction in the incidence of pests in SRI and the relative abundance of plant parasitic nematodes was low in SRI as compared to the NTP. About 31% and 37% saving in irrigation water was observed during Kharif and Rabi seasons, respectively in both methods of SRI cultivation over NTP. SRI performed well and consistently reduced requirement of inputs such as seed and water in different soil conditions. SRI method, using less water for rice production can help in overcoming water shortage in future and it can also make water available for growing other crops thus promoting crop diversificatio

A study of nanosized magnesium ferrite particles with high magnetic moment

Nano-sized magnesium ferrite particles were prepared by sol gel combustion synthesis and were either furnace cooled or quenched after calcining at various temperatures ranging from 300 to 800 degrees C. A magnetisation value of 61 emu/g was obtained at 5 K for sample calcined at 800 degrees C and quenched in liquid nitrogen temperature. This is one of the highest reported values of magnetisation obtained from quenching at such a lower temperature. An estimate of the number of Fe3+ ions on A and B sites was made after applying Neel Model on the magnetisation values measured at 5 K. It was estimated that Fe3+ ions segregates out from both sites disproportionately so as to cause a net decrease in the overall moment. The resultant cation distribution is found to be consistent with the coercivity data. (C) 2015 Elsevier B.V. All rights reserve

Novel ensemble machine learning models in flood susceptibility mapping

The research aims to propose the new ensemble models by combining the machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbour (KNN), boosted regression tree (BRT), and logitboost (LB) with the base classifier adabag (AB) for flood susceptibility mapping (FSM). The proposed models were implemented in the central west coast of India, which is vulnerable to flood events. For flood inventory mapping, a total of 210 flood localities were identified. Twelve effective factors were selected using the boruta algorithm for FSM. The area under the receiver operating characteristics (AUROC) curve and other statistical measures (sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and mean absolute error (MAE)) were employed to estimate and compare the success rate of the approaches. The validation results of the individual models in terms of AUC value were AB (92.74%) >RF (91.50%) >BRT (90.75%) >LB (89.07%) >NSC (88.97%) >KNN (83.88%), whereas the ensemble models showed that the AB-RF (94%) was of the highest prediction efficiency followed by, AB-KNN (93.33%), AB-NSC (93.02%), AB-LB (92.83%), and AB-BRT (92.64%). The outcomes of the ensemble models established that the AB is more appropriate to increase the accuracy of different single models. Therefore, this study can be useful for proper planning and management of the study area and flood hazard mapping in alike geographic environment

Application of machine learning techniques in groundwater potential mapping along the west coast of India

Groundwater potential mapping (GWPM) in the coastal zone is crucial for the planning and development of society and the environment. The current study is aimed to map the groundwater potential zones of Sindhudurg coastal stretch on the west coast of India, using three machine learning models: random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine (SVM). In order to achieve the objective, 15 groundwater influencing factors including elevation, slope, aspect, slope length (LS), profile curvature, plan curvature, topographical wetness index (TWI), distance from streams, distance from lineaments, lithology, geomorphology, soil, land use, normalized difference vegetation index (NDVI), and rainfall were considered for inter-thematic correlations and overlaid with spring and well occurrences in a spatial database. A total of 165 spring and well locations were identified, which had been divided into two classes: training and validation, at the ratio of 70:30, respectively. The RF, BRT, and RF-SVM ensemble models have been applied to delineate the groundwater potential zones and categorized into five classes, namely very high, high, moderate, low, and very low. RF, BRT, and ensemble model results showed that 33.3%, 35.6%, and 36.8% of the research area had a very high groundwater potential zone. These models were validated with area under the receiver operating characteristics (AUROC) curve. The accuracy of RF (94%) and hybrid model (93.4%) was more efficient than BRT (89.8%) model. In order to further evaluate and validate, four different sites were subsequently chosen, and we obtained similar results, ensuring the validity of the applied models. Additionally, ground-penetrating radar (GPR) technique was applied to predict the groundwater table and validated by measured wells. The mean difference between measured and GPR predicted groundwater table was 14 cm, which reflected the importance of GPR to guide the location of new wells in the study region. The outcomes of the study will help the decision-makers, government agencies, and private sectors for sustainable planning of groundwater in the area. Overall, the present study provides a comprehensive high-precision machine learning and GPR-based groundwater potential mapping

Study of magnesium ferrite nano particles with excess iron content

Stoichiometric and non stoichiometric magnesium ferrite (MgFe2+delta O-4,1, delta=0.0, 0.1) were synthesized by the sol gel combustion method resulting in nanocrystalline powders with size ranging from 10 to 100 nm. These powders were calcined at various temperatures (300-800 degrees C). One part of the calcined powder was quenched in liquid nitrogen and the other part furnace cooled, alpha-Fe2O3 was observed in all calcined samples by XRD and this was also reflected in the magnetization data. Electrical response of MgFe2.104 5. spinet phase to 75 ppm ethanol was found to be greater than that for a stoichiometric magnesium ferrite. (C) 2014 Elsevier B.V. All rights reserved

Effect of Oxygen Pressure on the Magnetic Properties of Yttrium-Iron-Garnet Thin Films Made by Pulsed Laser Deposition

Yttrium-iron-garnet (YIG) thin films were grown on quartz substrates using pulsed laser deposition (PLD) while varying oxygen gas pressure (P-O2) in the range of 6.5 x 10(-3) mbar to 1 mbar. YIG phase formation and magnetic properties were strongly dependent on P-O2. The YIG thin film grown at 1.7 x 10(-1) mbar shows the best magnetic properties (4 pi M-S approximate to 1650 G, low H-C approximate to 2 Oe) and FMR linewidth (Delta H approximate to 60 Oe) compared to films grown at other P-O2. Our results show that it is possible to obtain single-phase YIG films with low H-C and Delta H even on quartz substrates by adjusting the P-O2 during deposition