Long term monitoring and field testing of an innovative multi-storey timber building

Peer reviewedPostprin

A Survey on Clustering Algorithm for Microarray Gene Expression Data

The DNA data are huge multidimensional which contains the simultaneous gene expression and it uses the microarray chip technology, also handling these data are cumbersome. Microarray technique is used to measure the expression level from tens of thousands of gene in different condition such as time series during biological process. Clustering is an unsupervised learning process which partitions the given data set into similar or dissimilar groups. The mission of this research paper is to analyze the accuracy level of the microarray data using different clustering algorithms and identify the suitable algorithm for further research process

Angular Resolution of an EAS Array for Gamma Ray Astronomy at Energies Greater Than 5 x 10 (13) Ev

A 24 detector extensive air shower array is being operated at Ootacamund (2300 m altitude, 11.4 deg N latitude) in southern India for a study of arrival directions of showers of energies greater than 5 x 10 to the 13th power eV. Various configurations of the array of detectors have been used to estimate the accuracy in determination of arrival angle of showers with such an array. These studies show that it is possible to achieve an angular resolution of better than 2 deg with the Ooty array for search for point sources of Cosmic gamma rays at energies above 5 x 10 to the 13th power eV

Local Invariants and Pairwise Entanglement in Symmetric Multi-qubit System

Pairwise entanglement properties of a symmetric multi-qubit system are analyzed through a complete set of two-qubit local invariants. Collective features of entanglement, such as spin squeezing, are expressed in terms of invariants and a classifcation scheme for pairwise entanglement is proposed. The invariant criteria given here are shown to be related to the recently proposed (Phys. Rev. Lett. 95, 120502 (2005)) generalized spin squeezing inequalities for pairwise entanglement in symmetric multi-qubit states.Comment: 9 pages, 2 figures, REVTEX, Replaced with a published versio

Impact of Daily Arctic Sea Ice Variability in CAM3.0 during Fall and Winter

Climate projections suggest that an ice-free summer Arctic Ocean is possible within several decades and with this comes the prospect of increased ship traffic and safety concerns. The daily sea ice concentration tendency in five Coupled Model Intercomparison Project phase 5 (CMIP5) simulations is compared with observations to reveal that many models underestimate this quantity that describes high-frequency ice movements, particularly in the marginal ice zone. To investigate whether high-frequency ice variability impacts the atmosphere, the Community Atmosphere Model, version 3.0 (CAM3.0), is forced by sea ice with and without daily fluctuations. Two 100-member ensemble experiments with daily varying (DAILY) and smoothly varying (SMTH) sea ice are conducted, along with a climatological control, for an anoma- lously low ice period (August 2006–November 2007). Results are presented for three periods: September 2006, October 2006, and December–February (DJF) 2006/07. The atmospheric response differs between DAILY and SMTH. In September, sea ice differences lead to an anomalous high and weaker storm activity over northern Europe. During October, the ice expands equatorward faster in DAILY than SMTH in the Siberian seas and leads to a local response of near-surface cooling. In DJF, there is a 1.5-hPa positive sea level pressure anomaly over North America, leading to anomalous northerly flow and anomalously cool continental U.S. temperatures. While the atmospheric responses are modest, the differences arising from high temporal frequency ice variability cannot be ignored. Increasing the accuracy of coupled model sea ice variations on short time scales is needed to improve short-term coupled model forecasts

Intelligent Intrusion Detection System using Enhanced Arithmetic Optimization Algorithm with Deep Learning Model

The widespread use of interoperability and interconnectivity of computing systems is becoming indispensable for enhancing our day-to-day actions. The susceptibilities deem cyber-security systems necessary for assuming communication interchanges. Secure transmission needs security measures for combating the threats and required developments to security measures that counter evolving security risks. Though firewalls were devised to secure networks, in real-time they cannot detect intrusions. Hence, destructive cyber-attacks put forward severe security complexities, requiring reliable and adaptable intrusion detection systems (IDS) that could monitor unauthorized access, policy violations, and malicious activity practically. Conventional machine learning (ML) techniques were revealed for identifying data patterns and detecting cyber-attacks IDSs successfully. Currently, deep learning (DL) methods are useful for designing accurate and effective IDS methods. In this aspect, this study develops an intelligent IDS using enhanced arithmetic optimization algorithm with deep learning (IIDS-EAOADL) method. The presented IIDS-EAOADL model performs data standardization process to normalize the input data. Besides, equilibrium optimizer based feature selection (EOFS) approach is developed to elect an optimal subset of features. For intrusion detection, deep wavelet autoencoder (DWAE) classifier is applied. Since the proper tuning of parameters of the DWNN is highly important, EAOA algorithm is used to tune them. For assuring the simulation results of the IIDS-EAOADL technique, a widespread simulation analysis takes place using a benchmark dataset. The experimentation outcomes demonstrate the improvements of the IIDS-EAOADL model over other existing technique

On hydromagnetic flow due to a rotating disk with radiation effects

The effect of thermal radiation on the steady laminar convective hydromagnetic flow of a viscous and electrically conducting fluid due to a rotating disk of infinite extend is studied. The fluid is subjected to an external uniform magnetic field perpendicular to the plane of the disk. The governing Navier–Stokes and Maxwell equations of the hydromagnetic fluid, together with the energy equation, are transformed into nonlinear ordinary differential equations by using the von Karman similarity transformations. The resulting nonlinear ordinary differential equations are then solved numerically subject to the transformed boundary conditions by Runge–Kutta based shooting method. Comparisons with previously published works are performed and the results are found to be in excellent agreement. Numerical and graphical results for the velocity and temperature profiles as well as the skin friction and Nusselt number are presented and discussed for various parametric conditions

Export performance of Indian fisheries in the context of globalisation

Internationally traded fisheries products are characterised by a high degree of heterogenity, reflecting the wide range of species and of processing techniques. Indian sea food industry, by and large still remains as a supplier of raw materials to the pre processors in foreign countries and 90 per cent goes in bulk packs, which is the prime reason for the drastic reduction in the unit value realisation (Rao and Prakash, 1999). India 's share in the overall trade of the world is 1.5 to 2 per cent

जलवायु परिवर्तन के नियंत्रण में सामाजिक भूमिका (Societal role in curbing climate change-ClimEd Series:3B)

An increase in earth's average atmospheric temperature that causes corresponding changes in climate due to the greenhouse effect such as carbon dioxide emissions from burning fossil fuels / deforestation

Post-harvest soil nutrient prediction in hybrid castor (Ricinus communis l.) Cropping sequence using a multivariate analysis technique

In the era of precision agriculture, the fertilizer prescription based on the soil fertility status is much required.  Analyzing the soil after each crop is necessary for fertilizer recommendation and developing an alternative technique to forecast the soil available nutrient value rather than analyzing the soil. Multiple linear regression (MLR) equation was developed using filed experiment data to predict the soil available nutrient in castor cropping sequence. The post-harvest soil available nutrient was considered as the dependent variable and the initially available soil nutrient values, fertilizer added, yield and nutrient uptake of castor as an independent variable. In general, the post-harvest soil nutrient model's prediction accuracy was notable and had a coefficient of determination of less than 0.90. By calculating the RMSE (root means square error), R2 value, the ratio performance to deviation (RPD) and, RE (relative error) the performance of the MLR model was confirmed.Using the validated model, post-harvest soil available nutrients were predicted and compared with laboratory tested soil available nutreints. It turned out that the established model is more precisely effective and equally precise. Fertilizer recommendation could be made to subsequent crop after hybrid castor using the predicted soil available nutrients