Novel magnetic phases in a Gd2Ti2O7 pyrochlore for a field applied along the [100] axis

We report on longitudinal and transverse magnetisation measurements performed on single crystal samples of Gd2Ti2O7 for a magnetic field applied along the [100] direction. The measurements reveal the presence of previously unreported phases in fields below 10 kOe in an addition to the higher-field-induced phases that are also seen for H//[111], [110], and [112]. The proposed H-T phase diagram for the [100] direction looks distinctly different from all the other directions studied previously.Comment: 4 pages, 5 figure

The Kolar Schist Belt: A possible Archaean suture zone

The Kolar Schist Belt represents a N-S trending discontinuity in the structures, lithologies, and emplacement and metamorphic ages of late Archean gneisses. The suggestion of a much older basement on the west side of the belt is not seen on the east. Within the schist belt amphibolites from each side have distinctly different chemical characteristics, suggesting different sources at similar mantle depths. These amphibolites were probably not part of a single volcanic sequence, but may have formed about the same time in two completely different settings. Could the amphibolites with depleted light REE patterns represent Archean ocean floor volcanics which are derived from a mantle source with a long term depletion of the light REE? Why are the amphibolites giving an age which may be older than the exposed gneisses immediately on either side of the belt? These results suggest that it is necessary to seriously consider whether the Kolar Schist Belt may be a suture between two late Archean continental terranes

Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments

The main objective of online anomaly detection is to identify abnormal/unusual behavior such as network intrusions, malware infections, over utilized system resources due to design defects etc from real time data stream. Terrabytes of performance data generated in cloud data centers is a well accepted example of such data stream in real time. In this paper, we propose an online anomaly detection framework using non-parametric statistical technique in cloud data center. In order to determine the accuracy of the proposed work, we experiments it to data collected from RUBis cloud testbed and Yahoo Cloud Serving Benchmark (YCSB). Our experimental results shows the greater accuracy in terms of True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR) and False Negative Rate (FNR)

Self-assembly of iron nanoclusters on the Fe3O4(111) superstructured surface

We report on the self-organized growth of a regular array of Fe nanoclusters on a nanopatterned magnetite surface. Under oxidizing preparation conditions the (111) surface of magnetite exhibits a regular superstructure with three-fold symmetry and a 42 A periodicity. This superstructure represents an oxygen terminated (111) surface, which is reconstructed to form a periodically strained surface. This strain patterned surface has been used as a template for the growth of an ultrathin metal film. A Fe film of 0.5 A thickness was deposited on the substrate at room temperature. Fe nanoclusters are formed on top of the surface superstructure creating a regular array with the period of the superstructure. We also demonstrate that at least the initial stage of Fe growth occurs in two-dimensional mode. In the areas of the surface where the strain pattern is not formed, random nucleation of Fe was observed.Comment: 6 pages, 3 figure

Reliable and Automatic Recognition of Leaf Disease Detection using Optimal Monarch Ant Lion Recurrent Learning

Around 7.5 billion people worldwide depend on agriculture production for their livelihood, making it an essential component in keeping life alive on the planet. Negative impacts are being caused on the agroecosystem due to the rapid increase in the use of chemicals to combat plant diseases. These chemicals include fungicides, bactericides, and insecticides. Both the quantity and quality of the output are impacted when there is a high-scale prevalence of diseases in crops. Plant diseases provide a significant obstacle for the agricultural industry, which has a negative impact on the growth of plants and the output of crops. The problem of early detection and diagnosis of diseases can be solved for the benefit of the farming community by employing a method that is both quick and reliable regularly. This article proposes a model for the detection and diagnosis of leaf infection called the Automatic Optimal Monarch AntLion Recurrent Learning (MALRL) model, which attains a greater authenticity. The design of a hybrid version of the Monarch Butter Fly optimization algorithm and the AntLion Optimization Algorithm is incorporated into the MALRL technique that has been proposed. In the leaf image, it is used to determine acceptable aspects of impacted regions. After that, the optimal characteristics are used to aid the Long Short Term Neural Network (LSTM) classifier to speed up the process of lung disease categorization. The experiment's findings are analyzed and compared to those of ANN, CNN, and DNN. The proposed method was successful in achieving a high level of accuracy when detecting leaf disease for images of healthy leaves in comparison to other conventional methods