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This conceptual design report provides a detailed account of the European Spallation Source neutrino Super Beam (ESS$\nu$SB) feasibility study. This facility has been proposed after the measurements reported in 2012 of a relatively large value of the neutrino mixing angle $\theta_{13}$, which raised the possibility of observing potential CP violation in the leptonic sector with conventional neutrino beams. The measured value of $\theta_{13}$ also privileges the $2^{nd}$ oscillation maximum for the discovery of CP violation instead of the more typically studied $1^{st}$ maximum. The sensitivity at this $2^{nd}$ oscillation maximum is about three times higher than at the $1^{st}$ one, which implies a reduced influence of systematic errors. Working at the $2^{nd}$ oscillation maximum requires a very intense neutrino beam with an appropriate energy. The world's most intense pulsed spallation neutron source, the European Spallation Source (ESS), will have a proton linac operating at 5\,MW power, 2\,GeV kinetic energy and 14~Hz repetition rate (3~ms pulse duration, 4\% duty cycle) for neutron production. In this design study it is proposed to double the repetition rate and compress the beam pulses to the level of microseconds in order to provide an additional 5~MW proton beam for neutrino production. The physics performance has been evaluated for such a neutrino super beam, in conjunction with a megaton-scale underground water Cherenkov neutrino detector installed at a distance of 360--550\,km from ESS. The ESS proton linac upgrades, the accumulator ring required for proton-pulse compression, the target station design and optimisation, the near and far detector complexes, and the physics potential of the facility are all described in this report. The ESS linac will be operational by 2025, at which point the implementation of upgrades for the neutrino facility could begin., Comment: 216 pages

Transgalactosylation reaction is the penultimate step in the production of galactooligosaccharides (GOSs) which has prominent applications in the treatment of disorders. In the present study, partially purified β-galactosidase from Enterobacter aerogenes KCTC2190 was used for the synthesis of prebiotic GOSs. GOSs were produced using lactose as substrate. Structural elucidation of collected fractions of GOSs by liquid chromatography electrospray ionization mass spectrometry exhibited the appearance of major peaks of produced GOSs at m/z 241.20, 481.39, 365.11, 527.17, and 701.51 respectively. GOSs facilitated the growth of potential probiotic strains (Lactobacillus delbrueckii ssp. helveticus, Bifidobacterium bifidum, and Lactiplantibacillus plantarum) and liberated propionate and butyrate as principal short-chain fatty acids which established its prebiotic potency. Synbiotic combinations exhibited good antioxidant activities. Synbiotic combinations also exhibited antimicrobial activities against pathogenic microorganisms namely Staphylococcus aureus and Escherichia coli. Synbiotic combinations of GOSs and the respective probiotic microorganisms were able to decrease viable human bone cancer cells (MG-63).

High density polyethylene pipe has been used to draw coldwater for low temperature thermal desalination plants since the year 2006 at Lakshadweep Islands, India. The pipeline endured in-line oscillations due to shear current in one of the desalination plants, and this gave an opportunity to observe the response parameters of the prototype pipeline. The recorded oscillation parameters are utilized as a benchmark to verify the results of the vortex induced vibration (VIV) analysis in frequency domain and time domain, and the results show good concurrence. The in-line fatigue damage assessment is carried out for a range of current profiles showing the need for mitigation of pipeline VIV. The helical strakes on a partial length of the pipeline is modeled numerically showing significant suppression of VIV response as well as substantial increase in fatigue life of the pipeline. The benchmarking of the VIV response analysis with prototype measurements enhances the reliability of using numerical VIV fatigue analysis and mitigation measures.

Many failure and fracture studies of reactor pressure vessel material 20MnMoNi55 steel are performed in cryogenic conditions to study the scattered failure distribution property of the material in the lower self of ductile to brittle transition region. The experimental failure values of the RPV material are fitted with generalized extreme value (GEV) distribution and Weibull distribution through the commercial Statistical package R-i368 4.05 to study the nature of the distribution of fracture toughness in the lower self of ductile To brittle transition region. The nature of the distribution of fracture toughness of the RPV material 20MnMoNi55 steel is investigated from those experimental results. It is observed stochastically that the GEV distribution provides a better fit to the experimental failure data than the established Weibull distribution at least in the brittle portion of the DBT region of the tested material. This investigation is also supported by the two goodness of fit tests (Anderson–Darling test and Kolmogorov–Smirnov). Kolmogorov—Smirnov test results qualify Weibull distribution but indicated GEV as a better-fitted distribution of the experimental results. While Weibull distribution failed to qualify Anderson Darling test but GEV distribution qualifies the test. Therefore, GEV distribution can be thought, as a better option than Weibull distribution to capture the failure distribution for the RPV material in the lower self of the DBT region.

Effective biomarkers aid in the early diagnosis and monitoring of breast cancer and thus play an important role in the treatment of patients suffering from the disease. Growing evidence indicates that alteration of expression levels of miRNA is one of the principal causes of cancer. We analyze breast cancer miRNA data to discover a list of biclusters as well as breast cancer miRNA biomarkers which can help to understand better this critical disease and take important clinical decisions for treatment and diagnosis. In this paper, we propose a pattern-based parallel biclustering algorithm termed Rank-Preserving Biclustering (RPBic). The key strategy is to identify rank-preserved rows under a subset of columns based on a modified version of all substrings common subsequence (ALCS) framework. To illustrate the effectiveness of the RPBic algorithm, we consider synthetic datasets and show that RPBic outperforms relevant biclustering algorithms in terms of relevance and recovery. For breast cancer data, we identify 68 biclusters and establish that they have strong clinical characteristics among the samples. The differentially co-expressed miRNAs are found to be involved in KEGG cancer related pathways. Moreover, we identify frequency-based biomarkers (hsa-miR-410, hsa-miR-483-5p) and network-based biomarkers (hsa-miR-454, hsa-miR-137) which we validate to have strong connectivity with breast cancer. The source code and the datasets used can be found at http://agnigarh.tezu.ernet.in/~rosy8/Bioinformatics_RPBic_Data.rar . Graphical Abstract.

This paper deals with experimental procedures to determine thermal conductivity, heat capacity, and density of Pb–Sn (60/40) solder alloy between 27 and 280C. The relationship between temperature and these three physical properties was established. An indirect experimental procedure was developed following the Wiedemann Franz Law to determine the thermal conductivity of the material, while direct experimental approaches were used to obtain the specific heat and density values. The heat flow in our sample was analyzed by finite element modeling (FEM). Two different FEM cases for heat flow were analyzed, one with experimentally determined properties, while empirically developed relations were analyzed for the second case. We have also discussed the importance of determining physical properties at regular intervals of temperature for application in FEM, using the empirical relations. Thus, empirical relations can be used to model the high-temperature manufacturing processes like soldering, brazing, welding, and additive manufacturing with good accuracy.

Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it “Channel quality factor (CQF)” or ‘Z’. The other parameter known as “Communication expiration time” or ‘T’ together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives $$\sim $$ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to $$\sim $$ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

The wireless sensor network (WSN) is always known for its limited-energy issues and finding a good solution for energy minimization in WSNs is still a concern for researchers. Implementing mobility to the sink node is used widely for energy conservation or minimization in WSNs which reduces the distance between sink and communicating nodes. In this paper, with the intention to conserve energy from the sensor nodes, we designed a clustering based routing protocol implementing a mobile sink called ‘two dimensional motion of sink node (TDMS)’. In TDMS, each normal sensor node collects data and send it to their respective leader node called cluster head (CH). The sink moves in the two dimensional direction to collect final data from all CH nodes, particularly it moves in the direction to that CH which has the minimum remaining energy. The proposed protocol is validated through rigorous simulation using MATLAB and comparisons have been made with WSN’s existing static sink and mobile sink routing protocols over two different geographical square dimensions of the network. Here, we found that TDMS model gives the optimal result on energy dissipation per round and increased network lifetime.

The design of active delay circuits and variable delay elements is being investigated over the years as they are popular inside the integrated circuit chip, for example in on-chip clock distribution. The function of these kinds of circuit is to transfer the input signal at the output with an added amount of timing delay. For clock signals, it is important to achieve equal rise/fall time in order to support correct level-triggered-based on-chip sequential operation. However, most of the variable delay elements are unable to impart the matching of output rise/fall time. Therefore, in this article, we have unearthed a delay circuit which is expected to generate nearly equal rise/fall time at the output having a unique setup of delivering variable delay. A small-signal model for this proposed circuit is presented to note the related parameters for achieving the near-symmetric output rise/fall time. The circuit has been simulated in Cadence Virtuoso $$^{\textregistered }$$ for 90 nm Process Design Kit with an input signal of 1 GHz at 1.1 V power supply $$(V_{\mathrm{dd}})$$ . The simulation results assure that the expected functionality of our proposed variable delay architecture is sustained under different corner variations.

A routing protocol called ‘Centroid-Based Routing (CBR)’ is proposed to optimize the total system energy for a given wireless sensor network. We have designed the CBR protocol to optimize the battery life of the sensor nodes, by using a mobile Sink Node (SN). In CBR, several clusters are formed for the sensor nodes and each cluster is assigned a ‘Cluster Head (CH)’ node, and these CHs act as a local Base Station. The SN moves to a coordinate point (Xc, Yc) which we call a ‘Centroid Point (CP)’ to collect data from the CH nodes. This ‘CP’ is dependent on the coordinates of all the CHs and also on their residual or remaining energy left over at any given round. This way the CH nodes have to pump a balanced amount of energy to send and receive data from SN, which makes the nodes last for a longer period. The simulation results imply that the CBR model is much efficient compared to other existing models in terms of energy utilization and network lifetime for the non-uniformly distributed sensor nodes in a given network area.

In this paper, we are working to understand the statistical behavior of acoustic features of audio, for one of the low resource languages, which is Kokborok from North East India. First, we have developed a classification system for Kokborok music by using the traditional machine learning technique. We used mainly Timbre, Rhythm, and Intensity feature to classify songs between four classes having three subclasses. This classification system gives poor performance compared to other Indian languages and western languages. So, we develop a computational method to minimize the errors for each class for the overall system. For such poor low resource language, the ground truth set creation is very tough. So, the behavior of the audio features of each song is analyses mathematically to understand whether the truth set is correct or not. Technically the feature values have to be different for each class and in a similar range for subclasses. We have defined a statistical parameter called “alpha” (α), for estimating the better value of the accuracy rate. This parameter alpha eventually estimates the final accuracy rate. This alpha is calculated, and the final value of the accuracy rate was calculated by extrapolating when the number of songs goes to infinity. The method enhances the actual accuracy rate from 49 to 63%, in the limit when the number of samples goes to infinity. Overall, our approach, when used in conjunction with the machine learning method, can predict a better accuracy rate for Kokborok music.

A novel and miniaturized 4 × 4 butler matrix (BM) is proposed in this work. This proposed butler matrix is designed with four 3 dB quadrature (90°) forward wave coupled line coupler (FWCC) and two 45° Schiffman phase shifter. This BM is designed at center frequency of 2.4 GHz using FR4 substrate. The crossover of this BM has been replaced with a 0 dB FWCC. The performances of all couplers have been improved with the help of defected ground structures (DGS). Two C-shape and one I-shape DGSs are etched at the ground plane of each coupler, and two C-shape and one dumbbell shape DGSs are etched at the ground plane of 0 dB crossover which enhance the performance of the BM. This proposed BM provides a maximum bandwidth of 120 MHz at each port and occupies a total area of 0.677λ0 (84.74 mm) × 0.612λ0 (76.56 mm).

To promote diligent analysis of the progression of a disease, it is important to identify interesting biomarkers for the disease. Biclustering has already been established as an effective technique to help identify such biomarkers of high biological significance. Although in the recent past, a good number of biclustering techniques have been introduced, most of them fail to perform consistently across multiple domains or datasets. To choose a single biclustering technique that can help the accomplishment of such a critical task for multiple diseases with high precision is extremely difficult. Hence, in this study, we considered several biclustering techniques and accepted those techniques and their results which are found significant from enrichment perspective for subsequent analysis. Based on biclustering results, we constructed biological networks and carried out a topological, pathway and causal analysis on the modules extracted from the networks. Our multiobjective study enabled us to identify several biomarkers for esophageal squamous cell carcinoma (ESCC) such as IFNGR1, CLIC1, CDK4, and COPS5, after applying a ranking scheme.

Random forest (RF) is one of the most powerful ensemble classifiers often used in machine learning applications. It has been found successful on many benchmarked data. However, the performance of an RF model is highly affected by the calibration of the model parameters. It requires optimization of two parameters—(i) size of RF and (ii) number of features. RF is based on the principle of bagging and random selection of relevant features. This paper conveys an effective method in improving classification accuracy of RF. The principal component analysis (PCA) technique was used for dimension reduction of spectral bands whereas correlation-based feature selection (CFS) was used to identify the optimal set of features. RF was initialized by 10 random trees with an increment of 10, with a variable number of features till the model achieved its highest accuracy. The model was tested with variable sample sizes in order to observe the effectiveness. An investigation was carried out on Hyperion sensor data of the Earth Observing-1 (EO-1) satellite. The performance of RF was observed to be significantly enhanced in terms of predictive ability and computational expenses with the optimized set of features and number of random trees as base classifiers. While comparing with the other advanced classifiers like a support vector machine (SVM), multilayer perceptron (MLP) and maximum likelihood classifier (MLC), the optimized RF outperformed all the other classifiers.

In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset.It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens. Keywords: Coexpression network, Graphical processing unit, Module extraction, Generalized topological overlap measure

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. This aging-related disease occurs due to the degenerative loss of tissue or cellular functions in the brain and due to genetic and epigenetic effects. This study was conducted on an RNA-seq dataset of PD collected from BA9 tissues to get insights to PD. A few RNA-seq based transcriptomics studies on PD are available. However, most of these studies are limited to differential expression analysis, i.e., individual gene-based analysis that ignores interactions and associations among genes to establish the association with the disease. Here, we initially identify differentially expressed genes and then construct a co-expression network on detected genes to identify modules. Module preservation analysis is carried out to find the non-preserved modules. We identify a non-preserved module with 73 (70 are annotated) genes. Differential connectivity analysis, topological analysis, and functional enrichment analysis are performed to find the initial set of interesting genes. Our finding is that 42 (60%) genes are significantly enriched in pathways, biological processes, or molecular functions, and they are topologically interesting. Among these genes, 19 can be linked to the PD based on evidence from literature. They are considered as biomarkers for PD. From the remaining 23 genes, 11 are expressed in brain region. Therefore, these genes may be further explored to understand their roles in PD and can be considered as potential biomarkers.

A gene co-expression network (CEN) is of biological interest, since co-expressed genes share common functions and biological processes or pathways. Finding relationships among modules can reveal inter-modular preservation, and similarity in transcriptome, functional, and biological behaviors among modules of the same or two different datasets. There is no method which explores the one-to-one relationships and one-to-many relationships among modules extracted from control and disease samples based on both topological and semantic similarity using both microarray and RNA seq data. In this work, we propose a novel fusion measure to detect mapping between modules from two sets of co-expressed modules extracted from control and disease stages of Alzheimer's disease (AD) and Parkinson's disease (PD) datasets. Our measure considers both topological and biological information of a module and is an estimation of four parameters, namely, semantic similarity, eigengene correlation, degree difference, and the number of common genes. We analyze the consensus modules shared between both control and disease stages in terms of their association with diseases. We also validate the close associations between human and chimpanzee modules and compare with the state-ofthe- art method. Additionally, we propose two novel observations on the relationships between modules for further analysis.

This paper introduces an enhanced version of Pearson’s correlation coefficient (PCC) to achieve better biclustering-enabled co-expression analysis. The modified measure called local pearson correlation measure (LPCM) helps detect shifting, scaling, and shifting-and-scaling correlation patterns effectively over gene expression data in the presence of outlier. An LPCM-based biclustering technique called local correlation-based biclustering technique (LCBT) has also been proposed to identify biclusters of high biological significance. The biclustering results have been established both statistically and biologically using benchmarked gene expression data.

Master curve and reference temperature (T0) from three-point bending specimens of 20MnMoNi55 steel for different thicknesses and a/W ratios are determined using Kim Wallin’s master curve methodology (ASTM E1921-02) to study the effect of variation in thickness and a/W ratio at reference temperature (T0). Weibull stress at the crack tip is calculated from FE analysis of each fracture test using FE software ABAQUS. Calibration of Beremin parameters, like Weibull modulus (m) and scaling parameter (σu), and Cm,n is done using linear regression analysis of a large number of fracture test data at single test temperature. T0 for different thicknesses and a/W ratios are also evaluated from corresponding Weibull stress based on Beremin model using calibrated m, σu and Cm,n which are compared with experimental results showing case-specific good matching. The same calibrated values of Beremin parameters and Cm,n are also used to evaluate T0 for CT specimen of the same material using Beremin model, and an excellent matching with the experimental result is found.

The high speed wireline communication suffers from a lot of signal quality issues such as jitter and swing, which eventually leads to higher probability of data loss. As the current mode multiplexer, being the integral cell of any transceiver circuit guides to Serialize data in high rate, its arrangement is of utmost importance. This work explores a novel configuration of multiplexer embedded with cross-coupled NMOS latch after integrating the Transmission Gate (TG) principle with the MOS Current Mode Logic (MCML). The proposed configuration reads an average power, delay and power-delay product (PDP) of as tiny as 135.7 μW, 20.16 ps and 2.736 fJ, respectively when simulated for 90 nm CMOS using Cadence Virtuoso at 10 GHz switching frequency and 1 V power supply. The process variation is performed at different corners through Monte-Carlo runs with ‘no skew’ and ‘5% process skew’ variation at both pre-layout and post-layout to prove the robustness of the proposed Mux-Latch, which is employed to tender a new low gate count and energy efficient variation aware Serializer circuit capable of offering a data rate of as high as 50 Gbit/s. The entire circuit is also validated at lower technology nodes like 28 nm UMC.

A new Zn(II) coordination solid based on benzoate (Bz) and 3-aminopyridine (3‒Apy) viz., [Zn(3‒Apy)2(Bz)2] (1) has been synthesized and its crystal structure has been determined by single crystal X-ray diffraction analysis. The compound is characterized by IR, UV-Vis-NIR, thermal and elemental analysis. X-ray powder diffraction technology has been performed for the complex to investigate whether the analyzed crystal structure is truly representative of the bulk materials. The new compound crystallizes in the triclinic, $$P\stackrel{-}{1}$$ space group with unit cell dimensions: a = 10.0848(11) A, b = 11.0351(11) A, c = 11.4213(13) A. V = 1139.1(2) and Z = 2. Intermolecular N‒H⋯O and π‒π contacts between Zn(II) monomeric units in the crystal structure results in a supramolecular polymeric chain. Further, the extended 1D chain is self assembled via weak intermolecular C‒H⋯O hydrogen bonding interaction to result layered network structure. The interactions have been characterized by analyzing the topology of electron density within the realm of quantum theory of atoms in molecules and non-covalent interaction index calculations. A new Zn(II) coordination solid based on 3-aminopyridine and benzoic acid, [Zn(3‒Apy)2(Bz)2] (1) has been successfully synthesized. Intermolecular N‒H⋯O and π‒π contacts between Zn(II) monomeric units in the crystal structure results in a supramolecular polymeric chain. The extended 1D chain is self assembled via intermolecular C‒H⋯O hydrogen bonding interaction to result layered network structure.

This work shows the development of a lexicon for a poorly resourced language, namely Kokborok. Kokborok is a regional language of North East India and offers an entirely new base for research in music information retrieval (MIR) field. We first create a sentimental word dictionary known as lexicons to develop a polarity classification system. It is a text analysis work involving two types of lyrical features that are: ‘text stylistic feature’, and the features were taken out from the newly developed dictionary. We have also shown the comparative analysis with a various subset of music database based on their accuracy rate. After the system development, the experimental/simulations were done, and the results have been computationally analyzed. We performed linear extrapolation of the data taken by both the feature set, thus developing a dictionary. Text stylistic (TS) features have been observed to converge, at 52 and 39 percent respectively for the number of songs tending to infinity. It has been found that at present, it might be better to increase the features from the dictionary since it gives better accuracy for low resource language Kokborok.

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called CSC to detect protein complexes. The method is evaluated in terms of positive predictive value, sensitivity and accuracy using the datasets of the model organism, yeast and humans. CSC outperforms several other competing algorithms for both organisms. Further, we present a framework to establish the usefulness of CSC in analyzing the influence of a given disease gene in a complex topologically as well as biologically considering eight major association factors. Keywords: Protein complex, Connectivity, Semantic similarity, Contribution

Structural damage and its extent can be detected by vibration-based techniques to avoid failure or to minimize maintenance. Among different damage identification techniques, modal curvature approaches are widely researched and applied one. On the contrary, wavelet transformation (WT) methods are gaining popularity in damage identification of real life buildings because of their suitability for non-stationary signals and non-dependency on baseline data. This paper presents a novel approach utilizing complex continuous wavelet to effectively locate change in physical properties of reinforced concrete (RC) buildings by virtue of variation in frequency and mode shapes due to small change in mass and stiffness. In this paper, the effect of variation of mass and stiffness of a building on the modal parameters is established analytically using theequation of motion for a multi-degree freedom system under forced vibration condition. A 3-D finite element model was developed for predicting the modal frequencies and mode shapes of the scaled down six storey RC building and the effect of addition of mass on a particular level of structure on the modal parameters was studied. Further, acceleration time histories were recorded with variation in mass on 3rd story of building using wireless tri-axial accelerometers and the time histories were processed to arrive at Curvature Damage Factor and wavelet coefficients for identification of the additional load on the particular floor. Vibration responses from all floors of RC building in ambient and loaded conditions were analyzed for frequency response spectra (FRS). Mode shapes were drawn for unloaded case and loaded cases. It was observed that the modal frequency of building decreases with the increase in mass at floors. It is observed that CDF approach could detect the change in mass in both numerical and experimental results. However, CDF algorithm could not detect the addition of load in case 1, 2 and 3, i.e. when load was less than 25 kg, i.e. only 2.6% of floor mass (960 kg). The acquired data for the above stated load cases were analyzed using complex Gaussian ‘cgau5’ wavelet in MATLAB toolbox to determine the singularity in the signal in terms of wavelet coefficient modulus. It is observed that the WT approach is able to precisely locate the change in physical parameters of the RC model building. However, it is seen that additional load could not be detected in case where only 9 kg, i.e. 0.93% of the total floor mass, was placed on 3rd floor. From the research work, it is observed that CDF technique is inefficient in damage detection and always demand prior baseline information, which is usually difficult to obtain in practice. However, the wavelet transform-based approach more accurately detects the location of change without relying on intact state vibration data.

Novel QTL conferring resistance to both the SDS and SCN was detected in two RIL populations. Dual resistant RILs could be used in breeding programs for developing resistant soybean cultivars. Soybean cultivars, susceptible to the fungus Fusarium virguliforme, which causes sudden death syndrome (SDS), and to the soybean cyst nematode (SCN) (Heterodera glycines), suffer yield losses valued over a billion dollars annually. Both pathogens may occur in the same production fields. Planting of cultivars genetically resistant to both pathogens is considered one of the most effective means to control the two pathogens. The objective of the study was to map quantitative trait loci (QTL) underlying SDS and SCN resistances. Two recombinant inbred line (RIL) populations were developed by crossing ‘A95-684043’, a high-yielding maturity group (MG) II line resistant to SCN, with ‘LS94-3207’ and ‘LS98-0582’ of MG IV, resistant to both F. virguliforme and SCN. Two hundred F7 derived recombinant inbred lines from each population AX19286 (A95-684043 × LS94-3207) and AX19287 (A95-684043 × LS98-0582) were screened for resistance to each pathogen under greenhouse conditions. Five hundred and eighty and 371 SNP markers were used for mapping resistance QTL in each population. In AX19286, one novel SCN resistance QTL was mapped to chromosome 8. In AX19287, one novel SDS resistance QTL was mapped to chromosome 17 and one novel SCN resistance QTL was mapped to chromosome 11. Previously identified additional SDS and SCN resistance QTL were also detected in the study. Lines possessing superior resistance to both pathogens were also identified and could be used as germplasm sources for breeding SDS- and SCN-resistant soybean cultivars.

Whenever web-application executes dynamic SQL statements it may come under SQL injection attack. To evaluate the existing practices of its detection, we consider two different security scenarios for the web-application authentication that generates dynamic SQL query with the user input data. Accordingly, we generate two different datasets by considering all possible vulnerabilities in the run-time queries. We present proposed approach based on edit-distance to classify a dynamic SQL query as normal or malicious using web-profile prepared with the dynamic SQL queries during training phase. We evaluate the dataset using proposed approach and some well-known supervised classification approaches. Our proposed method is found more effective in detecting SQL injection attack under both the scenarios of authentication security.

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein-protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast and a few other model organisms. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called ComFiR to detect such protein complexes and further rank diseased complexes based on a query disease. We have shown that it has better performance in identifying protein complexes from human PPI data. This method is evaluated in terms of positive predictive value, sensitivity and accuracy. We have introduced a ranking approach and showed its application on Alzheimer's disease.

For speeding up query processing on Big Data, frequent sub-queries or views may be materialized such that the query processing cost is minimized with optimum cost of maintaining the materialized views and/or queries. Materializing frequent sub-queries and views means that resultant data set of the views reside in the memory of one or more nodes in the cluster, so that it reduces the MapReduce cost, submission and scheduling cost of Distributed File System jobs for query processing. We have defined materialized views as resultant data of frequent sub-queries and aggregation functions of a set of Big Data warehousing queries that are saved for enhancing query performance. The problem is defined as a multi-objective optimization problem for minimizing the total query processing MapReduce cost, MapReduce cost for maintaining the materialized views and the number of views selected for materializing with maximized total size of the views selected. We applied Differential Evolution algorithm and NSGA-II to study their performances for developing a recommendation system for selecting views for materializing in Big Data warehousing.

Analysis of gene expression patterns enables identification of significant genes related to a specific disease. We analyze gene expression data for esophageal squamous cell carcinoma (ESCC) using biclustering, gene–gene network topology and pathways to identify significant biomarkers. Biclustering is a clustering technique by which we can extract coexpressed genes over a subset of samples. We introduce a parallel and robust biclustering algorithm to identify shifted, scaled and shifted-and-scaled biclusters of high biological relevance. Additionally, we introduce a mapping algorithm to establish the module–bicluster relationship across control and disease stages and a hub-gene identification method to support our analysis framework. The C-CUDA implementation of our biclustering algorithm makes the method attractive due to faster speed and higher accuracy of results. Biomarkers such as CCNB1, CDK4, and KRT5 have been found to be closely associated with ESCC.

High-rate distributed denial of service (HDDoS) flooding attacks pose as a major threat to the Internet. Most present solutions based on machine learning approach are inept for detecting the attacks in real time due to high processing overhead. In this paper, we present a defense solution referred to as DyProSD that combines both the merits of feature-based and statistical approach to handle HDDoS flooding attacks. The statistical module marks the suspicious traffic and forwards to an ensemble of classifiers for ascertaining the traffic as malicious or normal. Our method filters the attack traffic protocol specifically by allocating various protocol specific filter engines dynamically. As and when DDoS attack occurs and the load of a filter engine reaches beyond its capable limit, a new filter engine is recruited dynamically from the idle resource pool for filtering, thus guaranteeing the quality of service for legitimate users concurrently. We establish the effectiveness of DyProSD through several experimental analysis and real-world dataset experiments and the results indicate enough confidence in favour of our solution.

Using a combination of phenotypic screening and molecular, statistical, and linkage analyses, we have mapped a dominant soybean rust resistance gene in soybean PI 567104B. Asian soybean rust (SBR), caused by the fungus Phakopsora pachyrhizi Syd. and P. Syd., is one of the most economically important diseases that affect soybean production worldwide. A long-term strategy for minimizing the effects of SBR is the development of genetically resistant cultivars. The objectives of the study were to identify the location of a rust-resistance (Rpp) gene(s) in plant introduction (PI) 567104B, and to determine if the gene(s) in PI 567104B was different from previously mapped Rpp loci. The progeny of the cross of 'IAR 2001 BSR' × PI 567104B was phenotyped from field assays of the F 2:3 and F 4:5 generations and from a growth chamber assay of 253 F 5:6 recombinant inbred lines (RILs). For the growth chamber, the phenotyping was conducted by inoculation with a purified 2006 fungal isolate from Mississippi. A resistance gene locus on PI 567104B was mapped to a region containing the Rpp6 locus on chromosome 18. The high level of resistance of F 1 plants from two other crosses with PI 567104B as one of the parents indicated that the gene from PI 567104B was dominant. The interval containing the gene is flanked by the simple sequence repeat (SSR) markers Satt131 and Satt394, and includes the SSR markers BARCSOYSSR_18_0331 and BARCSOYSSR_18_0380. The results also indicated that the resistance gene from PI 567104B is different from the Rpp1 to the Rpp4 genes previously identified. To determine if the gene from PI 567104B is different from the Rpp6 gene from PI 567102B, additional research will be required.

Soybean deploys multiple genetic mechanisms to confer tolerance to Fusarium virguliforme toxins. This study revealed that F. virguliforme culture filtrates could be used in mapping QTL underlying foliar SDS resistance. Sudden death syndrome (SDS) is a major soybean disease throughout most of the soybean growing regions in the world including the United States. The disease is caused by the fungal pathogen, Fusarium virguliforme (Fv). The fungus produces several toxins that are responsible for development of interveinal leaf chlorosis and necrosis, which are typical foliar SDS symptoms. Growing of resistant cultivars has been the most effective method in controlling the disease. The objective of the present study was to identify quantitative trait loci (QTL) underlying host responses of soybean to Fv toxins present in culture filtrates. To accomplish this objective, two recombinant inbred line (RIL) populations, AX19286 (A95-684043 × LS94-3207) and AX19287 (A95-684043 × LS98-0582), segregating for SDS resistance were evaluated for foliar symptom development by applying two screening protocols, the stem cutting and the root feeding assays. The AX19286 population revealed two major and seven minor QTL for SDS resistance. In the AX19287 population, we identified five major QTL and three minor QTL. The two QTL mapped to Chromosome 7 [molecular linkage group (MLG) M] and Chromosome 20 (MLG I) are most likely novel, and were detected through screening of the AX19287 population with stem cutting and root feeding assays, respectively. This study established that Fv culture filtrates could be employed in mapping QTL underlying foliar SDS resistance. The outcomes of the research also suggest that multiple genetic mechanisms might be used by soybean to overcome the toxic effects of the toxins secreted by the pathogen into culture filtrates.

Protein-protein interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. Identifying protein complexes is of great importance for understanding cellular organization and functions of organisms. In this work, a method is proposed, referred to as MIPCE, to find protein complexes in a PPI network based on mutual information.MIPCE has been biologically validated by GO-based score and satisfactory results have been obtained. We have also compared our method with some wellknown methods and obtained better results in terms of various parameters such as precession, recall and F-measure.

Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. This article presents a co-expression network construction and a biologically relevant network module extraction technique based on fuzzy set theoretic approach. The technique is able to handle both positive and negative correlations among genes. The constructed network for some benchmark gene expression datasets have been validated using topological internal and external measures. The effectiveness of network module extraction technique has been established in terms of well-known p-value, Q-value and topological statistics.

Many algorithms have been proposed recently to detect protein complexes in protein–protein interaction (PPI) networks. Most proteins form complexes to accomplish biological functions such as transcription of DNA, translation of mRNA and cell growth. Since proteins perform their tasks by interacting with each other, determining these protein–protein interactions is an important task. Traditional clustering approaches for protein complex identification cannot deal with noisy and incomplete PPI data and dependent on information from a single source. Since the noise in the interaction datasets hampers the detection of accurate protein complexes, we propose an ensemble approach for protein complex detection that attempts to combine information from Gene Ontology at the time of complex detection. The PPI data network is taken as input by several baseline complex detection algorithms to generate protein complexes. The protein complexes are then subsequently combined by the proposed ensemble using a consensus building module for the purpose of identifying meaningful complexes. The protein complexes thus predicted by the ensemble are evaluated by comparing them to a set of gold standard protein complexes and their biological relevance established using a co-localization score.

The soil borne fungus, Fusarium virguliforme, causes sudden death syndrome (SDS) in soybean, which is a serious foliar and root rot disease. The pathogen has never been isolated from the diseased foliar tissues; phytotoxins produced by the pathogen are believed to cause foliar SDS symptoms. One of these toxins, a 13.5-kDa acidic protein named FvTox1, has been hypothesized to interfere with photosynthesis in infected soybean plants and cause foliar SDS. The objective of this study is to determine if FvTox1 is involved in foliar SDS development. We created and studied five independent knockout fvtox1 mutants to study the function of FvTox1. We conducted Agrobacterium tumefaciens-mediated transformation to accomplish homologous recombination of FvTox1 with a hygromycin B resistance gene, hph, to generate the fvtox1 mutants. Approximately 40 hygromycin-resistant transformants were obtained from 10(6) conidial spores of the F. virguliforme Mont-1 isolate when the spores were co-cultivated with the A. tumefaciens EHA105 but not with LBA4044 strain carrying a recombinant binary plasmid, in which the hph gene encoding hygromycin resistance was flanked by 5'- and 3'-end FvTox1 sequences. We observed homologous recombination-mediated integration of hph into the FvTox1 locus among five independent fvtox1 mutants. In stem-cutting assays using cut soybean seedlings fed with cell-free F. virguliforme culture filtrates, the knockout fvtox1 mutants caused chlorophyll losses and foliar SDS symptoms, which were over twofold less than those caused by the virulent F. virguliforme Mont-1 isolate. Similarly, in root inoculation assays, more than a twofold reduction in foliar SDS development and chlorophyll losses was observed among the seedlings infected with the fvtox1 mutants as compared to the seedlings infected with the wild-type Mont-1 isolate. These results suggest that FvTox1 is a major virulence factor involved in foliar SDS development in soybean. It is expected that interference of the function of this toxin in transgenic soybean plants will lead to generation of SDS-resistant soybean cultivars.

Arsenic, the toxic metalloid, widely available in the natural ecosystem, poses serious problem through contaminated groundwater and drinking water. The emerging areas of arsenic hazards in agricultural systems through use of contaminated irrigation water and entry of toxin in crops has been largely overlooked. Arsenic accumulation by plants and its translocation to edible parts were observed to vary within crops and also across the cultivars. Wheat is an alternative choice of summer rice, due to low water requirement. With this background, the current experiment was conducted with four popular wheat cultivars to study the arsenic accumulation and varietal tolerance under different soils and groundwater. The arsenic content was determined by using atomic absorption spectrophotometer (AAS). Result revealed that, wheat cultivars differed in their grain arsenic concentration (0.23–1.22 mg kg−1), which differed across the sites and year of experiment. The arsenic translocation in wheat grains usually least, and accumulation by different tissues followed the order root > stem > leaf > grain across the cultivars. The cultivar UP-262 was found to accumulate least arsenic in grains and cultivar Kalyansona the highest under same growing condition, due to phyto-extraction or phyto-morphological potential of the varieties.

In soybean, the W4 gene encoding dihydroflavonol- 4-reductase controls anthocyanin pigment biosynthesis in flow- ers. The mutant allele, w4-m, is characterized by variegated flowers and was evolved from the insertion of an endogenous transposable element, Tgm9 ,i n intron II of theW4 gene. In the w4-m mutant line, reversion of the unstable allele from varie- gated to normal purple flower in revertants would indicate Tgm9's excision accompanied by its insertion into a second locus. We identified a male-sterile, female-sterile mutant from such germinal revertant bearing purple flowers. The objectives of our investigation were to map the sterility locus, identify candidate genes for the male-fertile, female-fertile phenotype, and then determine if sterility is associated with the insertion of Tgm9in the sterility locus. We usedbulked segregant analysisto map the locus to molecular linkage group J (chromosome 16). Fine mapping enabled us to flank the locus to a 62-kb region that contains only five predicted genes. One of the genes in that region, Glyma16g07850.1, codes for a helicase. A rice homolog of this gene has been shown to control crossing over and fertility phenotype. Thus, Glyma16g07850.1 is most likely the gene regulating the male and female fertility phenotype in soybean. DNA blot analysis of the segregating individuals for Tgm9 showed perfect association between sterility and the presence of the transposon. Most likely, the sterility mutation was caused by the insertion of Tgm9. The transposable element should facilitate identification of the male- and female-fertility gene. Characterization of the fertility gene will provide vital molecular insight on the reproductive biology of soybean and other plants.

Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. Usually big data tools perform computation in batch mode and are not optimized for iterative processing and high data dependency among operations. In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed. Similarly, graph-based architectures and in-memory big data tools have been developed to minimize I/O cost and optimize iterative processing. However, standard big data architectures are still lacking. Also appropriate tools are not available for many important bioinformatics problems, such as fast construction of co-expression and regulatory networks and salient module identification, detection of complexes over growing protein-protein interaction data, fast analysis of massive DNA, RNA, and protein sequence data, and fast querying on incremental and heterogeneous disease networks. This paper addresses the issues and challenges posed by several big data problems in bioinformatics, and gives an overview of the state of the art and the future research opportunities.

Design of earth retaining wall is an important problem in geotechnical engineering. A retaining wall may fail in sliding or rotation. In the present study, rotational mode of failure is considered. A new approach to compute the rotational displacement of gravity retaining wall under seismic condition is proposed. Seismic forces are computed using pseudo-dynamic method. Dry backfill soil is considered. The present solution is for horizontal ground surface. In the computation of rotational displacement, the location of rotating wall and shift in the point of application of all the associated forces after each time step is included which was ignored in earlier studies. From the present analysis, it is found that the rotational displacement depends on the characteristics of the input motion. A non-dimensional term is used to quantify the effect of input motion on the rotational displacement. Shear strength properties of the backfill soil and geometry of the wall plays very crucial role in computation of rotational displacement.

This work presents an effective hybrid classification approach for feature extraction from fused images of two different satellite sensors. Wavelet transform function was used to fuse the panchromatic Cartosat-I and multispectral Landsat ETM+ sensor’s images which could preserve both the spatial and spectral components of the original images. Multi-resolution segmentations based on homogeneity criterion formed the basis for the hybrid approach which uses supervised fuzzy NN approach of classifier in conjunction with knowledge classification system. Gaussian fuzzy membership function was defined on an optimal set of object features such as the Normalized Difference Vegetation Index, band mean, area, shape index and brightness derived from the segmented image objects for class description. Based on our kappa index analysis evaluation, the hybrid approach provides significantly better performance than its other counterparts such as artificial neural network, maximum likelihood classifier and support vector machine in terms of classification accuracy.

Oxidative stress is a common pathology seen in approximately half of all infertile men. In a normal situation, the seminal plasma contains antioxidant mechanisms which are likely to quench these reactive oxygen species. However, during infertility complications these antioxidant mechanisms may downplay and create a situation which is called oxidative stress.The aim of the present study was to assess the levels of lipid peroxide (LPO), protein peroxide (PPO) and activities of antioxidant enzymes superoxide dismutase (SOD), glutathione peroxidase (GPX) in blood and semen samples of an infertile male population from North-East India.We measured LPO, PPO, SOD and GPX in a total of 50 infertile individuals. For the study 20 fertile donors served as the control group.Patients with male factor infertility had significantly higher LPO and PPO levels (60.84 ± 3.55 and 72.84 ± 3.66; P 0.001) compared with controls (40.20 ± 4.33 and 59.93 ± 5.24) in blood. In semen also, the same trend was found with significantly higher LPO and PPO levels (200.27 ± 6.25 and 149.80 ± 11.47; P 0.001) compared with controls (116.51 ± 5.49 and 59.10 ± 4.62). The SOD and GPX enzymes in blood (3.40 ± 1.06 and 0.16 ± 0.01; P 0.001) and in semen (2.42 ± 1.32 and 0.24 ± 0.015; P 0.001) showed a significantly lower activity when compared with their respective controls (4.85 ± 0.78; 0.36 ± 0.05 and 4.24 ± 0.89; 0.65 ± 0.03). The SOD and GPX activity when compared with the LPO and PPO values, showed a positive correlation.We conclude that oxidative stress is associated with male factor infertility. This assessment may help in the treatment of this male infertility by suitable antioxidants.

In this paper, we introduce a connectivity-based protein complex finding method to find dense and sparse complexes along with core and periphery proteins. We named this method CNCM (Connectivity-based Network Clustering Method) as it uses clustering coefficient and the connectivity among nodes to find complexes. This method also ensures detection of protein complexes which are overlapping in nature. CNCM has been validated using both gold standard benchmark datasets and hand-curated PPI networks from publicly available sources. We evaluate and compare the performance of CNCM with several protein complex finding methods and found our results highly satisfactory.

Lower Gondwana coal from Garu-Gensi area in the West Siang district of Arunachal Pradesh in the Eastern Himalayas have been characterized with respect to their maceral constituents, mineral matter, ash composition, sulphurand trace-element contents. These are low-rank bituminous coals (V0 = 0.64) and their vitrinite content is about 60%. A first hand data with respect to twenty one trace-elements are reported. Our data indicate that these Lower Gondwana coals of extra-peninsular region are richer in terms of their trace-element content when compared with their counter parts of peninsular India.

Stem and root rot caused by the oomycete pathogen, Phytophthora sojae, is a serious soybean disease. Use of Phytophthora resistance genes (Rps) in soybean cultivars has been very effective in controlling this pathogen. Resistance encoded by Rps genes is manifested through activation of defense responses. In order to identify candidate signaling genes involved in the expression of Phytophthora resistance in soybean, a cDNA library was prepared from infected etiolated hypocotyl tissues of a Phytophthora resistant soybean cultivar harvested 2 and 4 h following P. sojae inoculation. In silico subtraction of 101,833 expressed sequence tags (ESTs) originating from unstressed cDNA libraries from 4,737 ESTs of this library resulted in identification of 204 genes that were absent in the unstressed libraries. Of the 204 identified genes, seven were P. sojae genes. Putative function of 91 of the 204 genes could not be assigned based on sequence comparison. Macroarray analyses of all 204 genes led to identification of 60 genes including 15 signaling-related soybean genes and three P. sojae genes, transcripts of which were induced twofold in P. sojae-infected tissues as compared to that in water controls. Eight soybean genes were down-regulated twofold following P. sojae infection as compared to water controls. Differential expression of a few selected genes was confirmed by conducting Northern and RT-PCR analyses. We have shown that two putative regulators of chromosome condensation 1 (RCC1) family proteins were down-regulated in the incompatible interaction. This observation suggested that the nucleocytoplasmic transport function for trafficking protein and non-coding RNA is suppressed during expression of race-specific Phytophthora resistance. Characterization of a cDNA library generated from tissues harvested almost immediately following P. sojae-infection of a resistant cultivar allowed us to identify many candidate signaling genes that are presumably involved in regulating the expression of defense-related pathways for expression of Phytophthora resistance in soybean.