Mono- and heterodimetallic FeII and RuII complexes based on a novel heteroditopic 4′-{bis(phosphanyl)aryl}-2,2′:6′,2″-terpyridine ligand

In the search for a versatile building block that allows the preparation of heteroditopic tpy-pincer bridging ligands, the synthon 14'-[C6H3(CH2Br)(2)-3,5]-2,2':6',2 ''-terpyridine was synthesized. Facile introduction of diphenylphosphanyl groups in this synthon gave the ligand 14'-[C6H3(CH2PPh2)2-3,5]-2,2':6',2"-terpyridine) ([tpyPC(H)Pj). The asymmetric mononuclear complex [Fe(tpy){tpyPC(H)P}](PF6)(2), prepared by selective coordination of [Fe(tpy)Cl-3] to the tpy moiety of [tpyPC(H)P], was used for the synthesis of the heterodimetallic complex [Fe(tpy)(tpyPCP)Ru(tpy)](PFC,)3, which applies the "complex as ligand" approach. Coordination of the ruthenium centre at the PC(H)P-pincer moiety of [Fe(tpy){tpyPC(H)P}](PF6)(2) has been achieved by applying a transcyclometallation procedure. The ground-state electronic properties of both complexes, investigated by cyclic and square-wave voltammetries and UV/Vis spectroscopy, are discussed and compared with those of [Fe(tPY)(2)](PF6)(2) and [Ru(PCP)(tpy)]Cl, which represent the mononuclear components of the heterodinuclear species. An in situ UV/Vis spectroelectrochemical study was performed in order to localize the oxidation and reduction steps and to gain information about the Fe-II-Ru-II communication in the heterodimetallic system [Fe(tpy)(tpyPCP)Ru(tpy)](PF6)(3) mediated by the bridging ligand [tpyPCP]. Both the voltammetric and spectroelectrochemical results point to only very limited electronic interaction between the metal centres in the ground state.

Givens rotation based fast backward elimination algorithm for RBF neural network pruning

A fast backward elimination algorithm is introduced based on a QR decomposition and Givens transformations to prune radial-basis-function networks. Nodes are sequentially removed using an increment of error variance criterion. The procedure is terminated by using a prediction risk criterion so as to obtain a model structure with good generalisation properties. The algorithm can be used to postprocess radial basis centres selected using a k-means routine and, in this mode, it provides a hybrid supervised centre selection approach.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Considering the normative, systemic and procedural dimensions in indicator-based sustainability assessments in agriculture

This paper develops a framework for evaluating sustainability assessment methods by separately analyzing their normative, systemic and procedural dimensions as suggested by Wiek and Binder [Wiek, A, Binder, C. Solution spaces for decision-making – a sustainability assessment tool for city-regions. Environ Impact Asses Rev 2005, 25: 589-608.]. The framework is then used to characterize indicator-based sustainability assessment methods in agriculture. For a long time, sustainability assessment in agriculture has focused mostly on environmental and technical issues, thus neglecting the economic and, above all, the social aspects of sustainability, the multifunctionality of agriculture and the applicability of the results. In response to these shortcomings, several integrative sustainability assessment methods have been developed for the agricultural sector. This paper reviews seven of these that represent the diversity of tools developed in this area. The reviewed assessment methods can be categorized into three types: (i) top-down farm assessment methods; (ii) top-down regional assessment methods with some stakeholder participation; (iii) bottom-up, integrated participatory or transdisciplinary methods with stakeholder participation throughout the process. The results readily show the trade-offs encountered when selecting an assessment method. A clear, standardized, top-down procedure allows for potentially benchmarking and comparing results across regions and sites. However, this comes at the cost of system specificity. As the top-down methods often have low stakeholder involvement, the application and implementation of the results might be difficult. Our analysis suggests that to include the aspects mentioned above in agricultural sustainability assessment, the bottomup, integrated participatory or transdisciplinary methods are the most suitable ones.

Normative, systemic and procedural aspects: a review of indicator‐based sustainability assessments in agriculture

Several methods for assessing the sustainability of agricultural systems have been developed. These methods do not fully: (i) take into account the multi‐functionality of agriculture; (ii) include multidimensionality; (iii) utilize and implement the assessment knowledge; and (iv) identify conflicting goals and trade‐offs. This paper reviews seven recently developed multidisciplinary indicator‐based assessment methods with respect to their contribution to these shortcomings. All approaches include (1) normative aspects such as goal setting, (2) systemic aspects such as a specification of scale of analysis, (3) a reproducible structure of the approach. The approaches can be categorized into three typologies. The top‐down farm assessments focus on field or farm assessment. They have a clear procedure for measuring the indicators and assessing the sustainability of the system, which allows for benchmarking across farms. The degree of participation is low, potentially affecting the implementation of the results negatively. The top‐down regional assessment assesses the on‐farm and the regional effects. They include some participation to increase acceptance of the results. However, they miss the analysis of potential trade‐offs. The bottom‐up, integrated participatory or transdisciplinary approaches focus on a regional scale. Stakeholders are included throughout the whole process assuring the acceptance of the results and increasing the probability of implementation of developed measures. As they include the interaction between the indicators in their system representation, they allow for performing a trade‐off analysis. The bottom‐up, integrated participatory or transdisciplinary approaches seem to better overcome the four shortcomings mentioned above.

An improvement in clear-air turbulence forecasting based on spontaneous imbalance theory: the ULTURB algorithm

Recent research has shown that Lighthill–Ford spontaneous gravity wave generation theory, when applied to numerical model data, can help predict areas of clear-air turbulence. It is hypothesized that this is the case because spontaneously generated atmospheric gravity waves may initiate turbulence by locally modifying the stability and wind shear. As an improvement on the original research, this paper describes the creation of an ‘operational’ algorithm (ULTURB) with three modifications to the original method: (1) extending the altitude range for which the method is effective downward to the top of the boundary layer, (2) adding turbulent kinetic energy production from the environment to the locally produced turbulent kinetic energy production, and, (3) transforming turbulent kinetic energy dissipation to eddy dissipation rate, the turbulence metric becoming the worldwide ‘standard’. In a comparison of ULTURB with the original method and with the Graphical Turbulence Guidance second version (GTG2) automated procedure for forecasting mid- and upper-level aircraft turbulence ULTURB performed better for all turbulence intensities. Since ULTURB, unlike GTG2, is founded on a self-consistent dynamical theory, it may offer forecasters better insight into the causes of the clear-air turbulence and may ultimately enhance its predictability.

Doll Play narratives about starting school in children of socially anxious mothers, and their relation to subsequent child school-based anxiety

Background: Child social anxiety is common, and predicts later emotional and academic impairment. Offspring of socially anxious mothers are at increased risk. It is important to establish whether individual vulnerability to disorder can be identified in young children. Method: The responses of 4.5 year-old children of mothers with social phobia (N = 62) and non-anxious mothers (N = 60) were compared, two months before school entry, using a Doll Play (DP) procedure focused on the social challenge of starting school. DP responses were examined in relation to teacher reports of anxious-depressed symptoms and social worries at the end of the child’s first school term. The role of earlier child behavioral inhibition and attachment, assessed at 14 months, was also considered. Results: Compared to children of non-anxious mothers, children of mothers with social phobia were significantly more likely to give anxiously negative responses in their school DP (OR = 2.57). In turn, negative DP predicted teacher reported anxious-depressed and social worry problems. There were no effects of infant behavioral inhibition or attachment. Conclusion: Vulnerability in young children at risk of anxiety can be identified using Doll Play narratives.

A procedure for the simultaneous determination of total nitrogen and total phosphorus in freshwater samples using persulphate microwave digestion

In the absence of a suitable method for routine analysis of large numbers of natural river water samples for organic nitrogen and phosphorus fractions, a new simultaneous digestion technique was developed, based on a standard persulphate digestion procedure. This allows rapid analysis of river, lake and groundwater samples from a range of environments for total nitrogen and phosphorus. The method was evaluated using a range of organic nitrogen and phosphorus structures tested at low, mid and high range concentrations from 2 to 50 mg l-1 nitrogen and 0.2 to 10 mg l-1 phosphorus. Mean recoveries for nitrogen ranged from 94.5% (2 mg I-1) to 92.7% (50 mg I-1) and for phosphorus were 98.2% (0.2 mg l-1) to 100.2% (10 mg l-1). The method is precise in its ability m reproduce results from replicate digestions, and robust in its ability to handle a variety of natural water samples in the pH range 5-8.

Information-based data selection for ensemble data assimilation

Ensemble-based data assimilation is rapidly proving itself as a computationally-efficient and skilful assimilation method for numerical weather prediction, which can provide a viable alternative to more established variational assimilation techniques. However, a fundamental shortcoming of ensemble techniques is that the resulting analysis increments can only span a limited subspace of the state space, whose dimension is less than the ensemble size. This limits the amount of observational information that can effectively constrain the analysis. In this paper, a data selection strategy that aims to assimilate only the observational components that matter most and that can be used with both stochastic and deterministic ensemble filters is presented. This avoids unnecessary computations, reduces round-off errors and minimizes the risk of importing observation bias in the analysis. When an ensemble-based assimilation technique is used to assimilate high-density observations, the data-selection procedure allows the use of larger localization domains that may lead to a more balanced analysis. Results from the use of this data selection technique with a two-dimensional linear and a nonlinear advection model using both in situ and remote sounding observations are discussed.

Multi-agent based modeling and simulation of complex system in hospital

This study puts forward a method to model and simulate the complex system of hospital on the basis of multi-agent technology. The formation of the agents of hospitals with intelligent and coordinative characteristics was designed, the message object was defined, and the model operating mechanism of autonomous activities and coordination mechanism was also designed. In addition, the Ontology library and Norm library etc. were introduced using semiotic method and theory, to enlarge the method of system modelling. Swarm was used to develop the multi-agent based simulation system, which is favorable for making guidelines for hospital's improving it's organization and management, optimizing the working procedure, improving the quality of medical care as well as reducing medical charge costs.

Palaeovegetation of China: a pollen data-based synthesis for the mid-Holocene and last glacial maximum.

Pollen data from China for 6000 and 18,000 14C yr bp were compiled and used to reconstruct palaeovegetation patterns, using complete taxon lists where possible and a biomization procedure that entailed the assignment of 645 pollen taxa to plant functional types. A set of 658 modern pollen samples spanning all biomes and regions provided a comprehensive test for this procedure and showed convincing agreement between reconstructed biomes and present natural vegetation types, both geographically and in terms of the elevation gradients in mountain regions of north-eastern and south-western China. The 6000 14C yr bp map confirms earlier studies in showing that the forest biomes in eastern China were systematically shifted northwards and extended westwards during the mid-Holocene. Tropical rain forest occurred on mainland China at sites characterized today by either tropical seasonal or broadleaved evergreen/warm mixed forest. Broadleaved evergreen/warm mixed forest occurred further north than today, and at higher elevation sites within the modern latitudinal range of this biome. The northern limit of temperate deciduous forest was shifted c. 800 km north relative to today. The 18,000 14C yr bp map shows that steppe and even desert vegetation extended to the modern coast of eastern China at the last glacial maximum, replacing today’s temperate deciduous forest. Tropical forests were excluded from China and broadleaved evergreen/warm mixed forest had retreated to tropical latitudes, while taiga extended southwards to c. 43°N.

Pollen-based biome reconstructions for China at 0 and 6000 years

Biomization provides an objective and robust method of assigning pollen spectra to biomes so that pollen data can be mapped and compared directly with the output of biomgeographic models. We have tested the applicability of this procedure, originally developed for Europe, to assign modern surface samples from China to biomes. The procedure successfully delineated the major vegetation types of China. When the same procedure was applied to fossil pollen samples for 6000 years ago, the reconstructions showed systematic differences from present, consistent with previous interpretations of vegetation changes since the mid-Holocene. In eastern China, the forest zones were systematically shifted northwards, such that cool mixed forests displaced taiga in northeastern China, while broad-leaved evergreen forest extended c. 300 km and temperate deciduous forestc. 500–600 km beyond their present northern limits. In northwestern China, the area of desert and steppe vegetation was reduced compared to present. On the Tibetan Plateau, forest vegetation extended to higher elevations than today and the area of tundra was reduced. These shifts in biome distributions imply significant changes in climate since 6000 years ago that can be interpreted qualitatively as a response to orbital forcing and its secondary effects on the Asian monsoon.

PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

Assessment of the relative reinforcing strength of cocaine in socially housed monkeys using a choice procedure.

Position in the social hierarchy can influence brain dopamine function and cocaine reinforcement in nonhuman primates during early cocaine exposure. With prolonged exposure, however, initial differences in rates of cocaine self-administration between dominant and subordinate monkeys dissipate. The present studies used a choice procedure to assess the relative reinforcing strength of cocaine in group-housed male cynomolgus monkeys with extensive cocaine self-administration histories. Responding was maintained under a concurrent fixed-ratio 50 schedule of food and cocaine (0.003-0.1 mg/kg per injection) presentation. Responding on the cocaine-associated lever increased as a function of cocaine dose in all monkeys. Although response distribution was similar across social rank when saline or relatively low or high cocaine doses were the alternative to food, planned t tests indicated that cocaine choice was significantly greater in subordinate monkeys when choice was between an intermediate dose (0.01 mg/kg) and food. When a between-session progressive-ratio procedure was used to increase response requirements for the preferred reinforcer (either cocaine or food), choice of that reinforcer decreased in all monkeys. The average response requirement that produced a shift in response allocation from the cocaine-associated lever to the food-associated lever was higher in subordinates across cocaine doses, an effect that trended toward significance (p = 0.053). These data indicate that despite an extensive history of cocaine self-administration, most subordinate monkeys were more sensitive to the relative reinforcing strength of cocaine than dominant monkeys.