Hierarchical nanoporous solid base catalysts for biofuels

Novel macroporous solid bases have been developed as alternative clean technologies to existing commercial homogeneous catalysts for the production of biodiesel from triglycerides; the latter suffer process disadvantages including complex separation and associated saponification and engine corrosion, and are unsuitable for continuous operation. To this end, tuneable macroporous MgAl hydrotalcites have been prepared by an alkali-free route and characterised by TGA, XRD, SEM and XPS. The macropore architecture improves diffusion of bulky triglyceride molecules to the active base sites, increasing activity. Lamellar and macroporous hydrotalcites will be compared for the transesterification of both model and plant oil feedstocks, and structure-reactivity relations identified.

Hierarchical porous catalysts tailored for biodiesel production

There is a pressing need for sustainable transportation fuels to combat both climate change and dwindling fossil fuel reserves. Biodiesel, synthesised from non-food plant (e.g., Jatropha curcas) or algal crops is one possible solution, but its energy efficient production requires design of new solid catalysts optimized for the bulky triglyceride and fatty acid feedstocks. Here we report on the synthesis of hierarchical macroporous-mesoporous silica and alumina architectures, and their subsequent functionalization by propylsulfonic acid groups or alkaline earth oxides to generate novel solid acid and base catalysts. These materials possess high surface areas and well-defined, interconnected macro-mesopore networks with respective narrow pore size distributions tuneable around 300 nm and 5 nm. Their high conductivity and improved mass transport characteristics enhance activity towards transesterification of bulky tricaprylin and palmitic acid esterification, over mesoporous analogues. This opens the way to the wider application of hierarchical catalysts in biofuel synthesis and biomass conversion.

Hierarchical control and performance regimes in local government

Purpose – The purpose of this paper is to consider hierarchical control as a mode of governance, and analyses the extent of control exhibited by central government over local government through the best value (BV) and comprehensive performance assessment (CPA) performance regimes. Design/methodology/approach – This paper utilises Ouchi's framework and, specifically, his articulation of bureaucratic or hierarchical control in the move towards achievement of organisational objectives. Hierarchical control may be inferred from the extent of “command and control” by Central Government, use of rewards and sanctions, and alignment to government priorities and discrimination of performance. Findings – CPA represents a more sophisticated performance regime than BV in the governance of local authorities by central government. In comparison to BV, CPA involved less scope for dialogue with local government prior to introduction, closer inspection of and direction of support toward poorer performing authorities, and more alignment to government priorities in the weightings attached to service blocks. Originality/value - The paper focuses upon the hierarchic/bureaucratic mode of governance as articulated by Ouchi and expands on this mode in order to analyse shifts in performance regimes in the public sector.

IPOs, clustering, indirect learning and filing independently

IPO underpricing has been attributed to valuation uncertainty, which can be at least partially resolved by the indirect learning associated with IPO clustering [Benveniste, L.M., Ljungqvist, A., Wilhelm, W.J., Yu, X.Y., 2003. Evidence of information spillovers in the production of investment banking services. Journal of Finance 58, 577–608]. We examine why firms might choose not to issue their IPOs contemporaneously with clusters of similar firms, forgoing opportunities to learn from their peers. We find that the willingness to file an IPO without the benefit of indirect learning from peer firm IPOs is directly related to insiders’ needs for portfolio diversification and the firm’s need to raise capital.

Surface modification of natural fibers using bacteria:depositing bacterial cellulose onto natural fibers to create hierarchical fiber reinforced nanocomposites

Triggered biodegradable composites made entirely from renewable resources are urgently sought after to improve material recyclability or be able to divert materials from waste streams. Many biobased polymers and natural fibers usually display poor interfacial adhesion when combined in a composite material. Here we propose a way to modify the surfaces of natural fibers by utilizing bacteria (Acetobacter xylinum) to deposit nanosized bacterial cellulose around natural fibers, which enhances their adhesion to renewable polymers. This paper describes the process of modifying large quantities of natural fibers with bacterial cellulose through their use as substrates for bacteria during fermentation. The modified fibers were characterized by scanning electron microscopy, single fiber tensile tests, X-ray photoelectron spectroscopy, and inverse gas chromatography to determine their surface and mechanical properties. The practical adhesion between the modified fibers and the renewable polymers cellulose acetate butyrate and poly(L-lactic acid) was quantified using the single fiber pullout test. © 2008 American Chemical Society.

Reliability of vegetation community information derived using decorana ordination and fuzzy c-means clustering

Descriptions of vegetation communities are often based on vague semantic terms describing species presence and dominance. For this reason, some researchers advocate the use of fuzzy sets in the statistical classification of plant species data into communities. In this study, spatially referenced vegetation abundance values collected from Greek phrygana were analysed by ordination (DECORANA), and classified on the resulting axes using fuzzy c-means to yield a point data-set representing local memberships in characteristic plant communities. The fuzzy clusters matched vegetation communities noted in the field, which tended to grade into one another, rather than occupying discrete patches. The fuzzy set representation of the community exploited the strengths of detrended correspondence analysis while retaining richer information than a TWINSPAN classification of the same data. Thus, in the absence of phytosociological benchmarks, meaningful and manageable habitat information could be derived from complex, multivariate species data. We also analysed the influence of the reliability of different surveyors' field observations by multiple sampling at a selected sample location. We show that the impact of surveyor error was more severe in the Boolean than the fuzzy classification. © 2007 Springer.

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches.

Multicriteria Evaluation and Optimization of Hierarchical Systems

It is shown that any multicriteria problem can be represented by a hierarchical system. Separate properties of the object are evaluated at the lower level of the system, using a criteria vector, and a composition mechanism is used to evaluate the object as a whole at the upper level. The paper proposes a method to solve complex multicriteria problems of evaluation and optimization. It is based on nested scalar convolutions of vector- valued criteria and allows simple structural and parametrical synthesis of multicriteria hierarchical systems.

A Hierarchical Architecture with Parallel Comunication for Implementing P Systems

Membrane systems are computational equivalent to Turing machines. However, its distributed and massively parallel nature obtain polynomial solutions opposite to traditional non-polynomial ones. Nowadays, developed investigation for implementing membrane systems has not yet reached the massively parallel character of this computational model. Better published approaches have achieved a distributed architecture denominated “partially parallel evolution with partially parallel communication” where several membranes are allocated at each processor, proxys are used to communicate with membranes allocated at different processors and a policy of access control to the communications is mandatory. With these approaches, it is obtained processors parallelism in the application of evolution rules and in the internal communication among membranes allocated inside each processor. Even though, external communications share a common communication line, needed for the communication among membranes arranged in different processors, are sequential. In this work, we present a new hierarchical architecture that reaches external communication parallelism among processors and substantially increases parallelization in the application of evolution rules and internal communications. Consequently, necessary time for each evolution step is reduced. With all of that, this new distributed hierarchical architecture is near to the massively parallel character required by the model.

Hierarchical Logical Description and Neural Recognition of Complex Patterns

Authors suggested earlier hierarchical method for definition of class description at pattern recognition problems solution. In this paper development and use of such hierarchical descriptions for parallel representation of complex patterns on the base of multi-core computers or neural networks is proposed.

A Partition Metric for Clustering Features Analysis

A new distance function to compare arbitrary partitions is proposed. Clustering of image collections and image segmentation give objects to be matched. Offered metric intends for combination of visual features and metadata analysis to solve a semantic gap between low-level visual features and high-level human concept.

HI!MVC: Hierarchical MVC Framework for WEB Applications

This paper presents Hi!MVC, a framework for developing high interactive web applications with a MVC Architecture. Nowadays, to manage, extend and correct web applications can be difficult due to the navigational paradigm they are based on. Hi!MVC framework helps to make these tasks easier. This framework allows building a web based interface, generating each page from the objects that represent its state. Every class to be showed in the interface is associated with two entities: its html representation (view) and its interactions in the view manager (controller). The whole html page is generated by composition of views according to the composition relationship of objects. Interactions between user and application are managed by the controller associated to the view which shows interaction elements (links or forms). Hi!MVC allows building web interface in a hierarchical and distributed way. There are other frameworks and APIs offering MVC architectures to web applications, but we think that they are not applying exactly the same concepts. While they keep on basing their architectures on the navigational paradigm we are offering a new point of view based on an innovator hierarchical model. First, we present the main ideas of our proposal. Next, we expose how to implement it using different Java technologies. Finally, we make a first approach to our hierarchical MVC model. We also compare shortly our proposal with the previously cited technologies.

Evolutionary Clustering of Complex Systems and Processes

In a paper the method of complex systems and processes clustering based use of genetic algorithm is offered. The aspects of its realization and shaping of fitness-function are considered. The solution of clustering task of Ukraine areas on socio-economic indexes is represented and comparative analysis with outcomes of classical methods is realized.

Model Construction and Research using System Compositional Approach on Natural Hierarchical Neural Networks. Development of Computer Toolbox

System compositional approach to model construction and research of informational processes, which take place in biological hierarchical neural networks, is being discussed. A computer toolbox has been successfully developed for solution of tasks from this scientific sphere. A series of computational experiments investigating the work of this toolbox on olfactory bulb model has been carried out. The well-known psychophysical phenomena have been reproduced in experiments.

Exploration by Means of Computer Simulation of Nonlinear Hierarchical Structure of Neuronal Memory on the Model of Olfactory Bulb

Results of numerical experiments are introduced. Experiments were carried out by means of computer simulation on olfactory bulb for the purpose of checking of thinking mechanisms conceptual model, introduced in [2]. Key role of quasisymbol neurons in processes of pattern identification, existence of mental view, functions of cyclic connections between symbol and quasisymbol neurons as short-term memory, important role of synaptic plasticity in learning processes are confirmed numerically. Correctness of fundamental ideas put in base of conceptual model is confirmed on olfactory bulb at quantitative level.