Microwave spectroscopic and theoretical studies on the phenylacetylene center dot center dot center dot H(2)O complex: C-H center dot center dot center dot O and O-H center dot center dot center dot pi hydrogen bonds as equal partners

Rotational spectra of five isotopologues of the title complex, C(6)H(5)CCH center dot center dot center dot H(2)O, C(6)H(5)CCH center dot center dot center dot HOD, C(6)H(5)CCH center dot center dot center dot D(2)O, C(6)H(5)CCH center dot center dot center dot H(2)(18)O and C(6)H(5)CCD center dot center dot center dot H(2)O, were measured and analyzed. The parent isotopologue is an asymmetric top with kappa = -0.73. The complex is effectively planar (ab inertial plane) and both `a' and `b' dipole transitions have been observed but no c dipole transition could be seen. All the transitions of the parent complex are split into two resulting from an internal motion interchanging the two H atoms in H(2)O. This is confirmed by the absence of such doubling for the C(6)H(5)CCH center dot center dot center dot HOD complex and a significant reduction in the splitting for the D(2)O analog. The rotational spectra, unambiguously, reveal a structure in which H(2)O has both O-H center dot center dot center dot pi (pi cloud of acetylene moiety) and C-H center dot center dot center dot O (ortho C-H group of phenylacetylene) interactions. This is in agreement with the structure deduced by IR-UV double resonance studies (Singh et al., J. Phys. Chem. A, 2008, 112, 3360) and also with the global minimum predicted by advanced electronic structure theory calculations (Sedlack et al., J. Phys. Chem. A, 2009, 113, 6620). Atoms in Molecule (AIM) theoretical analysis of the complex reveals the presence of both O-H center dot center dot center dot pi and C-H center dot center dot center dot O hydrogen bonds. More interestingly, based on the electron densities at the bond critical points, this analysis suggests that both these interactions are equally strong. Moreover, the presence of both these interactions leads to significant deviation from linearity of both hydrogen bonds.

Regionalization of precipitation in data sparse areas using large scale atmospheric variables - A fuzzy clustering approach

Delineation of homogeneous precipitation regions (regionalization) is necessary for investigating frequency and spatial distribution of meteorological droughts. The conventional methods of regionalization use statistics of precipitation as attributes to establish homogeneous regions. Therefore they cannot be used to form regions in ungauged areas, and they may not be useful to form meaningful regions in areas having sparse rain gauge density. Further, validation of the regions for homogeneity in precipitation is not possible, since the use of the precipitation statistics to form regions and subsequently to test the regional homogeneity is not appropriate. To alleviate this problem, an approach based on fuzzy cluster analysis is presented. It allows delineation of homogeneous precipitation regions in data sparse areas using large scale atmospheric variables (LSAV), which influence precipitation in the study area, as attributes. The LSAV, location parameters (latitude, longitude and altitude) and seasonality of precipitation are suggested as features for regionalization. The approach allows independent validation of the identified regions for homogeneity using statistics computed from the observed precipitation. Further it has the ability to form regions even in ungauged areas, owing to the use of attributes that can be reliably estimated even when no at-site precipitation data are available. The approach was applied to delineate homogeneous annual rainfall regions in India, and its effectiveness is illustrated by comparing the results with those obtained using rainfall statistics, regionalization based on hard cluster analysis, and meteorological sub-divisions in India. (C) 2011 Elsevier B.V. All rights reserved.

Fuzzy Clustering Based Ad Recommendation for TV Programs

Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called “degree of membership” calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score.

Minimization of Constraint Violation in Fuzzy Multiobjective Programming

Fuzzy multiobjective programming for a deterministic case involves maximizing the minimum goal satisfaction level among conflicting goals of different stakeholders using Max-min approach. Uncertainty due to randomness in a fuzzy multiobjective programming may be addressed by modifying the constraints using probabilistic inequality (e.g., Chebyshev’s inequality) or by addition of new constraints using statistical moments (e.g., skewness). Such modifications may result in the reduction of the optimal value of the system performance. In the present study, a methodology is developed to allow some violation in the newly added and modified constraints, and then minimizing the violation of those constraints with the objective of maximizing the minimum goal satisfaction level. Fuzzy goal programming is used to solve the multiobjective model. The proposed methodology is demonstrated with an application in the field of Waste Load Allocation (WLA) in a river system.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.

Implications of unitarity and analyticity for the D pi form factors

We consider the vector and scalar form factors of the charm-changing current responsible for the semileptonic decay D -> pi/nu. Using as input dispersion relations and unitarity for the moments of suitable heavy-light correlators evaluated with Operator Product Expansions, including O(alpha(2)(s)) terms in perturbative QCD, we constrain the shape parameters of the form factors and find exclusion regions for zeros on the real axis and in the complex plane. For the scalar form factor, a low-energy theorem and phase information on the unitarity cut are also implemented to further constrain the shape parameters. We finally propose new analytic expressions for the D pi form factors, derive constraints on the relevant coefficients from unitarity and analyticity, and briefly discuss the usefulness of the new parametrizations for describing semileptonic data.

A Refined Methodology for Durability-Based Service Life Estimation of Reinforced Concrete Structural Elements Considering Fuzzy and Random Uncertainties

A reliable method for service life estimation of the structural element is a prerequisite for service life design. A new methodology for durability-based service life estimation of reinforced concrete flexural elements with respect to chloride-induced corrosion of reinforcement is proposed. The methodology takes into consideration the fuzzy and random uncertainties associated with the variables involved in service life estimation by using a hybrid method combining the vertex method of fuzzy set theory with Monte Carlo simulation technique. It is also shown how to determine the bounds for characteristic value of failure probability from the resulting fuzzy set for failure probability with minimal computational effort. Using the methodology, the bounds for the characteristic value of failure probability for a reinforced concrete T-beam bridge girder has been determined. The service life of the structural element is determined by comparing the upper bound of characteristic value of failure probability with the target failure probability. The methodology will be useful for durability-based service life design and also for making decisions regarding in-service inspections.

Fuzzy logic based TCSC supplementary controller for damping of inter area oscillations in power system

This paper describes an application of a FACTS supplementary controller for damping of inter area oscillations in power systems. A fuzzy logic controller is designed to regulate a thyristor controlled series capacitor (TCSC) in a multimachine environment to produce additional damping in the system. Simultaneous application of the excitation controller and proposed controller is also investigated. Simulation studies have been done with different types of disturbances and the results are shown to be consistent with the expected performance of the supplementary controller.