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Delineation of management zones (MZs) are needed to manage fields in order to maximize economic return, minimize environmental impact, and improve soil and crop management. The MZs of uniform production potential may offer an effective solution to nutrient management. In this study, a total of 122 geo-referenced representative surface (0–250 mm depth) soil samples were collected from the study area covering an area of 6296 ha. Soil samples were analysed for pH, EC, CaCO3, organic carbon (SOC), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and micronutrients (Fe, Mn, Zn and Cu). Their spatial variability was analyzed and spatial distribution maps were constructed using geostatistical techniques. Geostatistical analysis showed that exponential, rational quadratic, tetraspherical, pentaspherical and circular models were the best-fit models for soil properties and available nutrients. Further, geographical weighted principal component analysis (GWPCA) and possibilistic fuzzy C-means (PFCM) clustering algorithm were carried out to delineate the management zones based on optimum clusters identified using fuzzy performance index (FPI) and normalized classification entropy (NCE). The results revealed that the optimum number of MZs for this study area was four and there was heterogeneity in soil nutrients in four MZs. The study indicated that MZ-based soil test crop response recommendation reduces the application quantity of fertilizer significantly at a large extent. Therefore, the management zone concept can reduce agricultural inputs and environmental pollution, and maximize crop production. [ABSTRACT FROM AUTHOR]

The ability to exploit students' sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students' data using machine learning techniques, and (ii) modelling and predicting students' emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders. [ABSTRACT FROM AUTHOR]

Business Management Education in India has shown an upward growth trend in the last couple of decades. Due to the diverse nature of the course, students from diverse academic backgrounds are being admitted to the course. Therefore, differences in students’ abilities and their learning styles have a significant effect on their learning outcomes. Meanwhile, with the development of learning technologies, learners can be provided a more effective learning environment to optimise their learning. The purpose of this study was to develop a model to automatically detect the students’ learning styles from their personal, academic and social media data and make recommendations for students, teachers, educators and administrators for overall improvement of learning outcomes. Data analysis in this research was represented using data collected from post-graduate business management students in India. A 10-fold cross-validation was used to create and test the models. The data were analysed by R and R-Studio. Classification accuracy, Precision, Recall, Kappa, ROC curve and F measure were observed. The results showed that the accuracy of classification by the C4.5 technique had the highest value at 95.7%, and it could be applied to develop Felder-Silverman’s learning style while taking into consideration students’ academic, personal information and social media preferences. [ABSTRACT FROM AUTHOR]

Over the last few years there has been an increased emphasis on conceptualising, developing, and implementing virtual Learning initiatives across the Indian education sector. Effective and successful use of Virtual Learning Environments (VLEs) in academic setting requires identifying and solving many important social and behavioural issues. Without identifying and addressing these issues, their use can compound the mistakes of the past and leave the learner with a passive, un-engaging experience, leading to incomplete learning and low performance. Therefore, educators are required to recognise learner’s attitude, readiness, and learning style to take advantage of VLE. This study was conducted to find interrelationship of student attitude, readiness, and learning style towards the effective use of VLE. A total of 240 students who are using any form of VLE for their learning and research volunteered for the research, and the scales were filled via the convenience sampling method. The scales of learning readiness, attitude, and learning style were administered to the participants. The relationship among the variables of the research and the research hypotheses for the model were tested using different statistical tools like correlation,t-test and regression. The research result revealed that there is a correlation among learning readiness, attitude, and learning style, and each of these factors influence the effective use of VLE. [ABSTRACT FROM PUBLISHER]

Focuses on the design of an extended complex Kalman filter for the measurement of power system frequency. Outline of the design principles and validity of the model; Application of the theory on standard test signals representing the worst-case measurement.

Mulberry (Morus indica L.) is a foliage crop cultivated extensively in the North-Eastern India under rainfed condition for rearing silkworm (Bombyx mori L.). An attempt was made through field experimentation to develop a soil-cum-nutrient management package for mulberry cultivation in acid soil under rainfed condition. Effects of different treatment combinations comprising of NPK, farmyard manure (FYM), lime and boron (B) on important soil properties, yield and quality of mulberry (Morus indica L. var. S1635) leaf were assessed to identify the best soil-cum-nutrient management package. Results indicated that fertilizer dosage based on ready-reckoner soil test value was not effective for the production of high-quality leaf with specific targeted yield of the crop (4.5 t/ha/crop-cutting). Integrated soil application of NPK @ 150:50:50 kg ha−1, FYM@10 t ha−1, lime as per requirement (3t ha−1 in present case) and foliar application of boric acid solution (0.1% w/v) was the most effective soil-cum-nutrient management package for the production of higher leaf yield which is comparable with the targeted yield. As far as leaf quality is concerned, increased moisture content, total N, P, K and B content of leaf are also attributed to the package. The package resulted in increased pH, organic carbon, dehydrogenase activity, available N, P, K and decreased exchangeable aluminum, exchangeable acidity and total potential acidity of soil. The study indicates the usefulness of soil-cum-nutrient management package for improved soil health of acid soils as well as achieving high yield and quality of mulberry leaf in such acid soils of the North-Eastern India and elsewhere. [ABSTRACT FROM AUTHOR]

Soil fertility has direct implications on the agricultural production scenarios of a region. Surface soil samples at 1 km grid were collected to assess the fertility status of Lakhimpur district (Assam) in North East India. Fertility parameters like soil organic carbon, available nitrogen, phosphorus, potassium, iron, manganese, zinc and copper were determined using standard analytical procedure. Spatial distribution maps of the soil parameters were generated using regularized spline method in ArcGIS 10.0. The average soil organic carbon content was 1.05% and the maximum area was under high availability status (78%). In the case of nitrogen, 57% of the area was under low availability status. In the case of available potassium and phosphorus, the areas under low availability status were 48% and 49% respectively. But for micronutrients, in general, the availability status was high except for zinc, which indicated that 40% of the area was under low availability. A methodology was developed to integrate the individual nutrient layers using a set of decision rules to identify the multinutrient deficient zones. The integrated map showed that 24% of the area had multiple nutrient deficiencies and fell under high priority zone that warrant immediate nutrient management interventions to mitigate the situation. [ABSTRACT FROM AUTHOR]