Benefits of Institutional & Collaborative Repositories Development in Academic Libraries

The paper deals with the concept of Institutional repositories, explains the need for establishment of repositories by academic libraries. Especially the university libraries, which are meant for catering the higher academic needs of researchers and teachers, have to procure books on diverse subjects and their serial holdings is also very large. Even the so-called large libraries are also finding it difficult to manage their overcrowded stacks which have resulted from an increase in scholarly publishing and the high cost of traditional library buildings. All this have made the repository an attractive option.Â

An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification

The aberration in human electrocardiogram (ECG) affects cardiovascular events that may lead to arrhythmias. Many automation systems for ECG classification exist, but the ambiguity to wisely employ the in-built feature extraction or expert based manual feature extraction before classification still needs recognition. The proposed work compares and presents the enactment of using machine learning and deep learning classification on time series sequences. The two classifiers, namely the Support Vector Machine (SVM) and the Bi-directional Long Short-Term Memory (BiLSTM) network, are separately trained by direct ECG samples and extracted feature vectors using multiresolution analysis of Maximal Overlap Discrete Wavelet Transform (MODWT). Single beat segmentation with R-peaks and QRS detection is also involved with 6 morphological and 12 statistical feature extraction. The two benchmark datasets, multi-class, and binary class, are acquired from the PhysioNet database. For the binary dataset, BiLSTM with direct samples and with feature extraction gives 58.1% and 80.7% testing accuracy, respectively, whereas SVM outperforms with 99.88% accuracy. For the multi-class dataset, BiLSTM classification accuracy with the direct sample and the extracted feature is 49.6% and 95.4%, whereas SVM shows 99.44%. The efficient statistical workout depicts that the extracted feature-based selection of data can deliver distinguished outcomes compared with raw ECG data or in-built automatic feature extraction. The machine learning classifiers like SVM with knowledge-based feature extraction can equally or better perform than Bi-LSTM network for certain datasets

Shear stress analysis of tubular composite beams subjected to bending by shear load

Tubular composite beams are of increasing interest due to their growing applications in the offshore and aerospace industries. Most analysis work done on tubular composite beams has been limited to pure bending, uniform axial loads or uniform torsion. These are also limited to the analysis of uniform section, uniform material and uniform thickness beams. In real applications, transverse shear loads are usually present and add complexity to the analyses. When a beam is under distributed or concentrated transverse loadings, regardless of the boundary conditions, the distributions of bending moments and internal transverse shear loads vary through the length of the beam. Analysis of such beams is very complicated. In this research, a systematic approach is presented to evaluate shear stress distribution across the cross section of thin walled tubular beams made of non homogeneous sections. Variation of shear stress through the thickness is ignored. Exact equations for the analysis of shear stresses in thin wall composite beams are derived in local coordinate systems. The results are projected in global coordinate system to facilitate evaluation and comparison of shear stress distribution in different beams. The method is applied to analyze beams with T, Triangular, Hexagonal, Octagonal and Decagonal sections. The pattern behaviour and shear stress variation in these beams is studied to predict the maximum shear stress in beams with circular cross section that has the same radius as the circumscribed circle of multi-gonal beam

Repellant effect of neem formulation and aqeuous extract of Melia azedarach on greenhouse whitefly (Trialeurodes vaporariorum Westwood, Hemiptera: Aleyrodidae)

The present study assessed the repellence activities of two biopesticides viz. a formulation of neem, neem baan and aqueous extract of Melia azedarach (Dharek) kernels against crawlers of greenhouse whitefly Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae). The maximum repellency (22.07%) was recorded at 10 % concentration of dharek extract followed by Neem Baan at 0.0025 % concentration (18.33%). The minimum repellency (4.71%) was observed at 0.0005 % concentration of Neem baan. These results indicate a potential use of neem baan and aqueous dharek kernel extracts in management of greenhouse whitefly

4-Prime cordiality of some cycle related graphs

Recently three prime cordial labeling behavior of path, cycle, complete graph, wheel, comb, subdivison of a star, bistar, double comb, corona of tree with a vertex, crown, olive tree and other standard graphs were studied. Also four prime cordial labeling behavior of complete graph, book, flower were studied. In this paper, we investigate the four prime cordial labeling behavior of corona of wheel, gear, double cone, helm, closed helm, butterfly graph, and friendship graph

Classical network theory aspect of no-pass filters

Call number: LD2668 .R4 1964 R16

Elimination of Wormhole Attacker Node in MANET Using Performance Evaluation Multipath Algorithm

In MANET, the more security is required in comparison to wired network. Wireless networks are susceptible to many attacks, including an attack known as the wormhole attack. The wormhole attack is very powerful, and preventing the attack has proven to be very difficult. In wormhole attacks, one malicious node tunnels packets from its location to the other malicious node. Such wormhole attacks result in a false route with fewer. If source node chooses this fake route, malicious nodes have the option of delivering the packets or dropping them. In this paper we specifically considering Wormhole attack. Instead  of  detecting  suspicious routes as  in  previous methods,  in this paper we  implement a  new  method which detects malicious nodes and works without modification of protocol,  using  a  hop-count  and time delay analysis  from  the users point of view without any special environment assumptions. The proposed work is simulated using OPNET and results showing the advantages of proposed work. Keywords: ad hoc network, hop-count analysis, network security, wormhole attack

Experimental and neural network approach to effective electrical conductivity of carbon nanotubes dispersed chiral nematic liquid crystals

Single walled carbon nanotubes (SWCNT’s) doped cholesteric liquid crystal composite has been prepared and characterized for their electrical responses. Also theoretically, an artificial neural network (ANN) approach has been trained for predicting the effective electrical conductivity of these composites. The ANN models are based on a feedforward backpropagation (FFBP) network with such training functions as the adaptive learning rate (GDX), gradient descent with adaptive learning rate (GDA), gradient descent (GD), conjugates gradient with Powell-Beale restarts (CGB), one-step secant (OSS), and Levenberg–Marquardt (LM), and training algorithms run at the uniform threshold transfer functions-Tangent sigmoid (TANSIG) and pure linear (PURELIN) for 1000 epochs. Our modeling confirms that the expected effective electrical conductivity by different training functions of ANN is in higher agreement with the experimental results of SWCNT doped CLC composites

Identification of genes associated with endocrine resistance in breast cancer

Resistance to tamoxifen, Faslodex and oestrogen-deprivation represents a major hurdle in breast cancer management, and determining the underlying factors driving resistant growth may improve treatment and prognosis. Expression microarrays (Atlas Plastic Human 12K Microarrays GeneSifter software) were used to identify genes altered in breast cancer models with acquired resistance to tamoxifen (TamR) or Faslodex (FasR) versus their parental MCF-7 cell line through cluster analysis, t-testing and ontological examination. Selected genes were verified by PCR, Western blotting and immunocytochemistry. Alongside known breast cancer-related genes (PEA3, vitronectin), two novel genes increased in resistance were the securin/cell-cycle regulator Pituitary Tumour-Transforming Gene-1 (PTTG1) (p=0.013 and p=0.013 in TamR and FasR cells respectively), and GDNF receptor-a3 (GFRa3) (p=0.014 in TamR cells) that promotes cell survival signalling via its coreceptor RET. Increased levels of PTTG1, GFRa3, or their family members were observed in further endocrine resistant states, including an additional faslodex-resistant model that has progressed to a highly-aggressive state (FasR-Lt) and cells resistant to oestrogen-deprivation (X-MCF-7). PTTG1 and GFRa3 induction in response to an anti-EGFR agent in the resistant models implicated these genes in limiting its growth inhibitory effect, and GFR<x3 ligand (arternin) was shown to overcome anti-EGFR response (78% growth recovery). mRNA studies in clinical disease revealed a significant association of PTTG1 with lymph node spread (p=0.001), high tumour grade (p=0.001) and proliferation (p<0.001), while GFRa3 was enriched in ER-negative tumours (p=0.01), showing loss of tubular differentiation (p=0.04) and expressing EGFR (p=0.013), profiles implying roles in clinical resistance and aggressive tumour behaviour. Promisingly, PTTG1 or GFRo3 siRNA significantly reduced cell growth (by 72% p=0.003 and 81% p=0.004 respectively), proliferative capacity (by 23% p<0.001 and 32% p<0.001 respectively) and induced apoptosis (by 43% p=0.05 and 103% p=0.05 respectively) in resistant models. Cumulatively, these data indicate PTTG1 and GFRa3 may provide useful biomarkers and perhaps new therapeutic targets for multiple resistant states