Impact of stratospheric variability on tropospheric climate change

An improved stratospheric representation has been included in simulations with the Hadley Centre HadGEM1 coupled ocean atmosphere model with natural and anthropogenic forcings for the period 1979–2003. An improved stratospheric ozone dataset is employed that includes natural variations in ozone as well as the usual anthropogenic trends. In addition, in a second set of simulations the quasi biennial oscillation (QBO) of stratospheric equatorial zonal wind is also imposed using a relaxation towards ERA-40 zonal wind values. The resulting impact on tropospheric variability and trends is described. We show that the modelled cooling rate at the tropopause is enhanced by the improved ozone dataset and this improvement is even more marked when the QBO is also included. The same applies to warming trends in the upper tropical troposphere which are slightly reduced. Our stratospheric improvements produce a significant increase of internal variability but no change in the positive trend of annual mean global mean near-surface temperature. Warming rates are increased significantly over a large portion of the Arctic Ocean. The improved stratospheric representation, especially the QBO relaxation, causes a substantial reduction in near-surface temperature and precipitation response to the El Chichón eruption, especially in the tropical region. The winter increase in the phase of the northern annular mode observed in the aftermath of the two major recent volcanic eruptions is partly captured, especially after the El Chichón eruption. The positive trend in the southern annular mode (SAM) is increased and becomes statistically significant which demonstrates that the observed increase in the SAM is largely subject to internal variability in the stratosphere. The possible inclusion in simulations for future assessments of full ozone chemistry and a gravity wave scheme to internally generate a QBO is discussed.

Equalisation with adaptive time lag

In an adaptive equaliser, the time lag is an important parameter that significantly influences the performance. Only with the optimum time lag that corresponds to the best minimum-mean-square-error (MMSE) performance, can there be best use of the available resources. Many designs, however, choose the time lag either based on preassumption of the channel or simply based on average experience. The relation between the MMSE performance and the time lag is investigated using a new interpretation of the MMSE equaliser, and then a novel adaptive time lag algorithm is proposed based on gradient search. The proposed algorithm can converge to the optimum time lag in the mean and is verified by the numerical simulations provided.

Adaptive least mean square CDMA detection with Gram–Schmidt pre-processing

The Gram-Schmidt (GS) orthogonalisation procedure has been used to improve the convergence speed of least mean square (LMS) adaptive code-division multiple-access (CDMA) detectors. However, this algorithm updates two sets of parameters, namely the GS transform coefficients and the tap weights, simultaneously. Because of the additional adaptation noise introduced by the former, it is impossible to achieve the same performance as the ideal orthogonalised LMS filter, unlike the result implied in an earlier paper. The authors provide a lower bound on the minimum achievable mean squared error (MSE) as a function of the forgetting factor λ used in finding the GS transform coefficients, and propose a variable-λ algorithm to balance the conflicting requirements of good tracking and low misadjustment.

Flexible MMSE DFE using length and LAG adaptation

This paper investigates how to choose the optimum tap-length and decision delay for the decision feedback equalizer (DFE). Although the feedback filter length can be set as the channel memory, there is no closed-form expression for the feedforward filter length and decision delay. In this paper, first we analytically show that the two dimensional search for the optimum feedforward filter length and decision delay can be simplified to a one dimensional search, and then describe a new adaptive DFE where the optimum structural parameters can be self-adapted.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Nickel(II) and copper(II) complexes of Schiff base ligands containing N4O2 and N4S2 donors with pyrrole terminal binding groups: synthesis, characterization, X-ray structures, DFT and electrochemical studies

A series of hexadentate ligands, H2Lm (m = 1−4), [1H-pyrrol-2-ylmethylene]{2-[2-(2-{[1H-pyrrol-2-ylmethylene]amino}phenoxy)ethoxy]phenyl}amine (H2L1), [1H-pyrrol-2-ylmethylene]{2-[4-(2-{[1H-pyrrol-2-ylmethylene]amino}phenoxy)butoxy]phenyl}amine (H2L2), [1H-pyrrol-2-ylmethylene][2-({2-[(2-{[1H-pyrrol-2-ylmethylene]amino}phenyl)thio]ethyl}thio)phenyl]amine (H2L3) and [1H-pyrrol-2-ylmethylene][2-({4-[(2-{[1H-pyrrol-2-lmethylene]amino}phenyl)thio]butyl}thio) phenyl]amine (H2L4) were prepared by condensation reaction of pyrrol-2-carboxaldehyde with {2-[2-(2-aminophenoxy)ethoxy]phenyl}amine, {2-[4-(2-aminophenoxy)butoxy]phenyl}amine, [2-({2-[(2-aminophenyl)thio]ethyl}thio)phenyl]amine and [2-({4-[(2-aminophenyl)thio]butyl}thio)phenyl]amine respectively. Reaction of these ligands with nickel(II) and copper(II) acetate gave complexes of the form MLm (m = 1−4), and the synthesized ligands and their complexes have been characterized by a variety of physico-chemical techniques. The solid and solution states investigations show that the complexes are neutral. The molecular structures of NiL3 and CuL2, which have been determined by single crystal X-ray diffraction, indicate that the NiL3 complex has a distorted octahedral coordination environment around the metal while the CuL2 complex has a seesaw coordination geometry. DFT calculations were used to analyse the electronic structure and simulation of the electronic absorption spectrum of the CuL2 complex using TDDFT gives results that are consistent with the measured spectroscopic behavior of the complex. Cyclic voltammetry indicates that all copper complexes are electrochemically inactive but the nickel complexes with softer thioethers are more easily oxidized than their oxygen analogs.

Measuring the discrepancy between fecundity and lifetime reproductive success in a pollinating fig wasp

Lifetime reproductive success in female insects is often egg- or time-limited. For instance in pro-ovigenic species, when oviposition sites are abundant, females may quickly become devoid of eggs. Conversely, in the absence of suitable oviposition sites, females may die before laying all of their eggs. In pollinating fig wasps (Hymenoptera: Agaonidae), each species has an obligate mutualism with its host fig tree species [Ficus spp. (Moraceae)]. These pro-ovigenic wasps oviposit in individual ovaries within the inflorescences of monoecious Ficus (syconia, or ‘figs’), which contain many flowers. Each female flower can thus become a seed or be converted into a wasp gall. The mystery is that the wasps never oviposit in all fig ovaries, even when a fig contains enough wasp females with enough eggs to do so. The failure of all wasps to translate all of their eggs into offspring clearly contributes to mutualism persistence, but the underlying causal mechanisms are unclear. We found in an undescribed Brazilian Pegoscapus wasp population that the lifetime reproductive success of lone foundresses was relatively unaffected by constraints on oviposition. The number of offspring produced by lone foundresses experimentally introduced into receptive figs was generally lower than the numbers of eggs carried, despite the fact that the wasps were able to lay all or most of their eggs. Because we excluded any effects of intraspecific competitors and parasitic non-pollinating wasps, our data suggest that some pollinators produce few offspring because some of their eggs or larvae are unviable or are victims of plant defences.

New advances in aircraft MRO services: data mining enhancement

Aircraft Maintenance, Repair and Overhaul (MRO) agencies rely largely on row-data based quotation systems to select the best suppliers for the customers (airlines). The data quantity and quality becomes a key issue to determining the success of an MRO job, since we need to ensure we achieve cost and quality benchmarks. This paper introduces a data mining approach to create an MRO quotation system that enhances the data quantity and data quality, and enables significantly more precise MRO job quotations. Regular Expression was utilized to analyse descriptive textual feedback (i.e. engineer’s reports) in order to extract more referable highly normalised data for job quotation. A text mining based key influencer analysis function enables the user to proactively select sub-parts, defects and possible solutions to make queries more accurate. Implementation results show that system data would improve cost quotation in 40% of MRO jobs, would reduce service cost without causing a drop in service quality.

Conformational modulation of sequence recognition in synthetic macromolecules

The different triplet sequences in high molecular weight aromatic copolyimides comprising pyromellitimide units ("I") flanked by either ether-ketone ("K") or ether-sulfone residues ("S") show different binding strengths for pyrene-based tweezer-molecules. Such molecules bind primarily to the diimide unit through complementary π-π-stacking and hydrogen bonding. However, as shown by the magnitudes of 1H NMR complexation shifts and tweezer-polymer binding constants, the triplet "SIS" binds tweezer-molecules more strongly than "KIS" which in turn bind such molecules more strongly than "KIK". Computational models for tweezer-polymer binding, together with single-crystal X-ray analyses of tweezer-complexes with macrocyclic ether-imides, reveal that the variations in binding strength between the different triplet sequences arise from the different conformational preferences of aromatic rings at diarylketone and diarylsulfone linkages. These preferences determine whether or not chain-folding and secondary π−π-stacking occurs between the arms of the tweezermolecule and the 4,4'-biphenylene units which flank the central diimide residue.

The development and impact of the Livestock Guru: meeting the knowledge needs of poor livestock keepers in Tamil Nadu, India

Livestock keepers comprise 2/3rds of the 2.8 billion households living on less than two dollars per day. However, as a group they tend to be marginalised and excluded from formal service provision, particularly in relation to animal health. Therefore, the following paper describes the development of the Livestock Guru, a multi-media learning programme created to meet the knowledge needs of poor livestock keepers in Tamil Nadu, India. The findings from the study illustrate the importance of both appropriate visuals, voice-overs but also the need for addressing issues in the environment in which learning will take place.

Improving aircraft maintenance, repair, and overhaul: A novel text mining approach

Aircraft Maintenance, Repair and Overhaul (MRO) feedback commonly includes an engineer’s complex text-based inspection report. Capturing and normalizing the content of these textual descriptions is vital to cost and quality benchmarking, and provides information to facilitate continuous improvement of MRO process and analytics. As data analysis and mining tools requires highly normalized data, raw textual data is inadequate. This paper offers a textual-mining solution to efficiently analyse bulk textual feedback data. Despite replacement of the same parts and/or sub-parts, the actual service cost for the same repair is often distinctly different from similar previously jobs. Regular expression algorithms were incorporated with an aircraft MRO glossary dictionary in order to help provide additional information concerning the reason for cost variation. Professional terms and conventions were included within the dictionary to avoid ambiguity and improve the outcome of the result. Testing results show that most descriptive inspection reports can be appropriately interpreted, allowing extraction of highly normalized data. This additional normalized data strongly supports data analysis and data mining, whilst also increasing the accuracy of future quotation costing. This solution has been effectively used by a large aircraft MRO agency with positive results.