Implication of a sigma-Methane Complex en Route to Elimination of Methane from a Ruthenium Complex: An Experimental and Theoretical Investigation

The five-coordinated 16-electron complex Ru(Me)(dppe)(2)]OTf] (3) undergoes methane elimination at room temperature to afford the ortho-metalated species (dppe){(C6H5)(C6H4)PCH2CH2P(C6H5)(2)}Ru]OTf] (7). Methane elimination, monitored using NMR spectroscopy, revealed no intermediate throughout the reaction. The NOE between Ru-Me protons and ortho phenyl protons and an agostic interaction trans to the methyl group were found in complex 3 by NMR spectroscopy, which form the basis for three plausible pathways for methane elimination and ortho metalation: pathway I (through spatial interaction), pathway II (through oxidative addition and reductive elimination), and pathway III (through agostic interaction). Methane elimination from complex 3 via pathway I was discounted, since it involves interactions through space and not through bonds. Moreover, the calculated energy barrier for the pathway I transition state was quite high (71.3 kcal/mol), which also indicates that this pathway is very unlikely. Furthermore, no spectroscopic evidence for oxidatively added seven-coordinated Ru(IV) species was found and the computed energy barrier of the transition state for pathway II was moderately high (41.1 kcal/mol), which suggests that this cannot be the right pathway for methane elimination and ortho-metalation of complex 3. On the other hand, indirect evidence in the form of chemical reactions point to the most plausible pathway for methane elimination, pathway III, via the intermediacy of a sigma-CH4 complex that could not be found spectroscopically. DFT calculations at several levels on this pathway showed an initial low-barrier rearrangement through TS1 to a square-pyramidal intermediate wherein methyl and agostic C-H are cis to each other. Migration of hydrogen from agostic C-H and elimination of methane proceed through the transition state TS2, which retains a weak metal-H bonding through most parts of the reaction coordinate. Upon comparison of all three pathways, pathway III was found to be the most likely for methane elimination and ortho-metalation of complex 3.

Porous membranes designed from bi-phasic polymeric blends containing silver decorated reduced graphene oxide synthesized via a facile one-pot approach

In this work, porous membranes were designed by selectively etching the PEO phase, by water, from a melt-mixed PE/PEO blend. The pure water flux and the resistance across the membrane were systematically evaluated by employing an indigenously developed cross flow membrane setup. Both the phase morphology and the cross sectional morphology of the membranes was assessed by scanning electron microscopy and an attempt was made to correlate the observed morphology with the membrane performance. In order to design antibacterial membranes for water purification, partially reduced graphene oxide (rGO), silver nanoparticles (Ag) and silver nanoparticles decorated with rGO (rGO-Ag) were synthesized and incorporated directly into the blends during melt mixing. The loss of viability of bacterial cells was determined by the colony counting method using E. coli as a model bacterium. SEM images display that the direct contact with the rGO-Ag nanoparticles disrupts the cell membrane. In addition, the rGO-Ag nanoparticles exhibited a synergistic effect with respect to bacterial cell viability in comparison to both rGO and Ag nanoparticles. The possible mechanism associated with the antibacterial activity in the membranes was discussed. This study opens new avenues in designing antibacterial membranes for water purification.

Analytical approach to quantile estimation in regional frequency analysis based on fuzzy framework

Regional frequency analysis is widely used for estimating quantiles of hydrological extreme events at sparsely gauged/ungauged target sites in river basins. It involves identification of a region (group of watersheds) resembling watershed of the target site, and use of information pooled from the region to estimate quantile for the target site. In the analysis, watershed of the target site is assumed to completely resemble watersheds in the identified region in terms of mechanism underlying generation of extreme event. In reality, it is rare to find watersheds that completely resemble each other. Fuzzy clustering approach can account for partial resemblance of watersheds and yield region(s) for the target site. Formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix obtained based on the approach. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies. In this study, it is analytically shown that the strategy results in under-prediction of quantiles. To address this, a mathematical approach is proposed in this study and its effectiveness in estimating flood quantiles relative to DFA and TS is demonstrated through Monte-Carlo simulation experiments and case study on Mid-Atlantic water resources region, USA. (C) 2015 Elsevier B.V. All rights reserved.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Study of Wrist Pulse Signals Using a Bi-Modal Gaussian Model

Wrist pulse signals contain important information about the health of a person and hence diagnosis based on pulse signals has assumed great importance. In this paper we demonstrate the efficacy of a two term Gaussian model to extract information from pulse signals. Results have been obtained by conducting experiments on several subjects to record wrist pulse signals for the cases of before exercise and after exercise. Parameters have been extracted from the recorded signals using the model and a paired t-test is performed, which shows that the parameters are significantly different between the two groups. Further, a recursive cluster elimination based support vector machine is used to perform classification between the groups. An average classification accuracy of 99.46% is obtained, along with top classifiers. It is thus shown that the parameters of the Gaussian model show changes across groups and hence the model is effective in distinguishing the changes taking place due to the two different recording conditions. The study has potential applications in healthcare.

Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright (c) 2015 John Wiley & Sons, Ltd.

Reservoir Operation with Fuzzy State Variables for Irrigation of Multiple Crops

This paper presents the development and application of a stochastic dynamic programming model with fuzzy state variables for irrigation of multiple crops. A fuzzy stochastic dynamic programming (FSDP) model is developed in which the reservoir storage and soil moisture of the crops are considered as fuzzy numbers, and the reservoir inflow is considered as a stochastic variable. The model is formulated with an objective of minimizing crop yield deficits, resulting in optimal water allocations to the crops by maintaining storage continuity and soil moisture balance. The standard fuzzy arithmetic method is used to solve all arithmetic equations with fuzzy numbers, and the fuzzy ranking method is used to compare two or more fuzzy numbers. The reservoir operation model is integrated with a daily-based water allocation model, which results in daily temporal variations of allocated water, soil moisture, and crop deficits. A case study of an existing Bhadra reservoir in Karnataka, India, is chosen for the model application. The FSDP is a more realistic model because it considers the uncertainty in discretization of state variables. The results obtained using the FSDP model are found to be more acceptable for the case study than those of the classical stochastic dynamic model and the standard operating model, in terms of 10-day releases from the reservoir and evapotranspiration deficit. (C) 2015 American Society of Civil Engineers.

Bi-Modal DRAM Cache: Improving Hit Rate, Hit Latency and Bandwidth

In this paper, we present Bi-Modal Cache - a flexible stacked DRAM cache organization which simultaneously achieves several objectives: (i) improved cache hit ratio, (ii) moving the tag storage overhead to DRAM, (iii) lower cache hit latency than tags-in-SRAM, and (iv) reduction in off-chip bandwidth wastage. The Bi-Modal Cache addresses the miss rate versus off-chip bandwidth dilemma by organizing the data in a bi-modal fashion - blocks with high spatial locality are organized as large blocks and those with little spatial locality as small blocks. By adaptively selecting the right granularity of storage for individual blocks at run-time, the proposed DRAM cache organization is able to make judicious use of the available DRAM cache capacity as well as reduce the off-chip memory bandwidth consumption. The Bi-Modal Cache improves cache hit latency despite moving the metadata to DRAM by means of a small SRAM based Way Locator. Further by leveraging the tremendous internal bandwidth and capacity that stacked DRAM organizations provide, the Bi-Modal Cache enables efficient concurrent accesses to tags and data to reduce hit time. Through detailed simulations, we demonstrate that the Bi-Modal Cache achieves overall performance improvement (in terms of Average Normalized Turnaround Time (ANTT)) of 10.8%, 13.8% and 14.0% in 4-core, 8-core and 16-core workloads respectively.

Structural and Optical Modification in Bi doped As40S60 Thin Films.

The influence of substitution of Bi atom instead of S atoms on the structural and optical properties of thin films of As40S60 are reported. The density is found to be increased with the addition Bi heavy metal into As2S3. The amorphous to polycrystalline structure of the bulk sample is observed for Bi more than 7%. The glass transition temperature is found to be decreased with addition of Bi. The absorption edge shifts to shorter wavelength, thereby decreasing optical band gap of BixAs(40)S(60-x) (x= 0,2 and 4% here) film. The optical parameter change is discussed from the stand point of chemical bonds formed in the films and related to the defect states produced due to incorporation of Bi atoms in place of chalcogenide S atoms.

Delineation of homogeneous hydrometeorological regions using wavelet-based global fuzzy cluster analysis

Identification of homogeneous hydrometeorological regions (HMRs) is necessary for various applications. Such regions are delineated by various approaches considering rainfall and temperature as two key variables. In conventional approaches, formation of regions is based on principal components (PCs)/statistics/indices determined from time series of the key variables at monthly and seasonal scales. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore, delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they do not provide effective representation of the key variables when the records exhibit non-stationarity. Consequently, the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed in this article. The approach considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its non-stationarity (if any). Effectiveness of the proposed approach in forming HMRs is demonstrated by application to India, as there are no prior attempts to form such regions over the country. Drought severity-area-frequency (SAF) curves are constructed corresponding to each of the newly formed regions for the use in regional drought analysis, by considering standardized precipitation evapotranspiration index (SPEI) as the drought indicator.

Fuzzy State Real-Time Reservoir Operation Model for Irrigation with Gridded Rainfall Forecasts

A short-term real-time operation model with fuzzy state variables is developed for irrigation of multiple crops based on earlier work on long-term steady-state policy. The features of the model that distinguish it from the earlier work are (1) apart from inclusion of fuzziness in reservoir storage and in soil moisture of crops, spatial variations in rainfall and soil moisture of crops are included in the real-time operation model by considering gridded command area with a grid size of 0.5 degrees latitude by 0.5 degrees longitude; (2) the water allocation model and soil moisture balance equations are integrated with the real-time operation model with consideration of ponding water depth for Paddy crop; the model solution specifies reservoir releases for irrigation in a 10-day time period and allocations among the crops on a daily basis at each grid by maintaining soil moisture balance at the end of the day; and (3) the release policy is developed using forecasted daily rainfall data of each grid and is implemented for the current time period using actual 10-day inflow and actual daily rainfall of each grid. The real-time operation model is applied to Bhadra Reservoir in Karnataka, India. The results obtained using the real-time operation model are compared with those of the standard operating policy model. Inclusion of fuzziness in reservoir storage and soil moisture of crops captures hydrologic uncertainties in real time. Considerations of irrigation decisions on a daily basis and the gridded command area result in variations in allocating water to the crops, variations in actual crop evapotranspiration, and variations in soil moisture of the crops on a daily basis for each grid of the command area. (C) 2015 American Society of Civil Engineers.

Temperature-Dependent Photoluminescence of g-C3N4: Implication for Temperature Sensing

We report the temperature-dependent photoluminescence (PL) properties of polymeric graphite-like carbon nitride (g-C3N4) and a methodology for the determination of quantum efficiency along with the activation energy. The PL is shown to originate from three different pathways of transitions: sigma*-LP, pi*-LP, and pi*-pi, respectively. The overall activation energy is found to be similar to 73.58 meV which is much lower than the exciton binding energy reported theoretically but ideal for highly sensitive wide-range temperature sensing. The quantum yield derived from the PL data is 23.3%, whereas the absolute quantum yield is 5.3%. We propose that the temperature-dependent PL can be exploited for the evaluation of the temperature dependency of quantum yield as well as for temperature sensing. Our analysis further indicates that g-C3N4 is well-suited for wide-range temperature sensing.

Fuzzy Approach to Rank Global Climate Models

Eleven coupled model intercomparison project 3 based global climate models are evaluated for the case study of Upper Malaprabha catchment, India for precipitation rate. Correlation coefficient, normalised root mean square deviation, and skill score are considered as performance indicators for evaluation in fuzzy environment and assumed to have equal impact on the global climate models. Fuzzy technique for order preference by similarity to an ideal solution is used to rank global climate models. Top three positions are occupied by MIROC3, GFDL2.1 and GISS with relative closeness of 0.7867, 0.7070, and 0.7068. IPSL-CM4, NCAR-PCMI occupied the tenth and eleventh positions with relative closeness of 0.4959 and 0.4562.

Study of coexisting phases in Bi doped La0.67Sr0.33MnO3

We report the remarkable phase separation behavior in La0.67Sr0.33MnO3 doped with Bi3+ ion at La site. The temperature dependent resistivity and magnetization of La0.67-xBixSr0.33MnO3 (x>0) show the presence of phase separation of ferromagnetic metallic and charge ordered antiferromagnetic insulating phases. Markedly, the field dependant magnetization studies of La0.67-xBixSr0.33MnO3 (x=0.3) show the metamagnetic nature of ferromagnetic metallic state implying the competition of coexisting ferromagnetic metallic and charge ordered antiferromagnetic phases. The electron spin resonance and exchange bias studies of La0.67-xBixSr0.33MnO3 (x=0.4 and 0.5) substantiate the coexistence of ferromagnetic clusters in antiferromagnetic matrix. (C) 2016 Elsevier B.V. All rights reserved.

Prussian blue as a single precursor for synthesis of Fe/Fe3C encapsulated N-doped graphitic nanostructures as bi-functional catalysts

We report a unique, single source precursor Prussian blue (iron(III) ferrocyanide (Fe-4(III)Fe-II(CN)(6)](3))) for the synthesis of Fe/Fe3C nanoparticle encapsulated N-doped graphitic layers and bamboo-like graphitic nanotubes. Hollow N-doped graphite (N-HG) nanostructures are obtained when the encapsulated nanostructures are treated with an acid. Both the encapsulated nanostructures and N-HG are shown to be applicable as bi-functional electrocatalysts for oxygen reduction (ORR) and oxygen evolution reactions (OER). The ORR activity is shown to be improved for N-HG and is comparable to commercial Pt/C. On the other hand, encapsulated nanostructures exhibit OER activity with long-term stability comparable to commercial RuO2.