Improvement of the stoichiometric network analysis for determination of instability conditions of complex nonlinear reaction systems

Stoichiometric network analysis (SNA), a known method for analyzing complex reaction systems including biochemical ones, is improved and applied to a nonlinear process studied far from equilibrium in a continuously fed, well stirred tank reactor (CSTR). A particular attention is focused on the determination of the narrow range of the control parameter values where the main steady state is unstable and where different dynamic states can be simulated numerically. The instability region, the most important feature of nonlinear reaction systems, is calculated as a function of the SNA parameters (current rates and reciprocal concentrations of intermediate species in the steady state) and simplified by retaining only the dominant terms. Since the number of the current rates is usually larger than the number of linearly independent equations to be used for their calculation, it is shown here that the current rates can be replaced with a smaller number of reaction rates at the steady state. These rates are related to the experimental data in a simple manner. The instability condition is also written as a function of dimensionless parameters derived from the SNA. This general approach is applied to a model of the Bray–Liebhafsky (BL) reaction having seven reactions without direct autocatalysis or autoinhibition, studied under CSTR conditions. Since the model has six intermediate species, it would be very difficult to analyze its instability condition by the conventional procedure, where a sixth order characteristic equation would have to be solved. On the other hand, the instability condition, obtained easily by the improved SNA, locates correctly the oscillatory region using numerical integration. Other dynamic states found earlier with a larger model of the BL reaction, such as mixed-mode oscillations, period doubling and chaos, are also obtained within the theoretically predicted oscillatory region. Thus, besides the general advantages of the improved stoichiometric network analysis as a method appropriate for the examination of complex nonlinear reactions, we show that the various mentioned dynamic states can be obtained by a very simple variant of the model of the BL reaction realized under CSTR conditions.     

Implicit Meshes for Effective Silhouette Handling

Using silhouettes in uncontrolled environments typically requires handling occlusions as well as changing or cluttered backgrounds, which limits the applicability of most silhouette based methods. For the purpose of 3-D shape modeling, we show that representing generic 3-D surfaces as implicit surfaces lets us effectively address these issues. This desirable behavior is completely independent from the way the surface deformations are parame-trized. To show this, we demonstrate our technique in three very different cases: Modeling the deformations of a piece of paper represented by an ordinary triangulated mesh; reconstruction and tracking a person’s shoulders whose deformations are expressed in terms of Dirichlet Free Form Deformations; reconstructing the shape of a human face parametrized in terms of a Principal Component Analysis model. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. This work was supported in part by the Swiss National Science Foundation     

The Use of Object-Oriented Modeling for Water Resources Planning in Egypt

Water problems are omnipresent and are already becoming a limiting factor in the development of many countries. Currently the balance between the available and required water in Egypt is fragile. Any movement away from the balancing point means either less ambitious economic development or depletion of the resources and degradation of the environment. The continuing revolution in computer hardware and software is expected to provide more insight into the water problems and to alleviate some of the future water crises. In this paper we have investigated potential benefits which can be accrued from the application of object-oriented modeling in water resources.     

Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.     

Oily water treatment using a new steady-state fiber-bed coalescer

This study was concerned with the possibility of oil separation from two heavily polluted wastewater types: formation water and wastewater from hardening shop, using bed a newly developed coalescer. Experiments were carried out using original wastewaters and an artificial model wastewater. Results obtained for seven samples of formation water of very different quality showed that the water properties had no significant effect on bed coalescence efficiency. In contrast to this, crude oil properties strongly influenced steady-state bed coalescence. In the treatment of hardening oily wastewater in situ during a 4-month period oil concentration in the effluent was less than 20mg/l in all experiments. It appeared that oil concentration and water quality had no effect on bed separation efficiency. Special design of the coalescer and use of two filter materials ensured its good performance. Namely, the pipe-in-pipe construction provided the water orientation change several times while passing through the unit, making inertia one of dominant separation mechanisms.     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.     

Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.