Mathematical Analysis of Chemical Reaction Systems

The use of mathematical methods for the analysis of chemical reaction systems has a very long history, and involves many types of models: deterministic versus stochastic, continuous versus discrete, and homogeneous versus spatially distributed. Here we focus on mathematical models based on deterministic mass-action kinetics. These models are systems of coupled nonlinear differential equations on the positive orthant. We explain how mathematical properties of the solutions of mass-action systems are strongly related to key properties of the networks of chemical reactions that generate them, such as specific versions of reversibility and feedback interactions.     

Adsorption studies of Cr(III) ions from aqueous solutions by DEHPA impregnated onto Amberlite XAD7 – Factorial design analysis

The present paper investigates the adsorption of Cr(III) ions using the SIR, prepared by impregnation of Amberlite XAD7 with di-(2-ethylhexyl)-phosphoric acid (DEHPA), which has been chosen as an extractant for the purpose of this study. The Amberlite XAD7–DEHPA resin was impregnated with DEHPA and ethylic alcohol as solvent trough dynamic column impregnation method. The influence of different physicochemical parameters (pH, resin dosage, initial concentration of Cr(III) ions, contact time and temperature) upon the adsorption capacity of XAD7–DEHPA, in the Cr(III) ions removal process from aqueous solution, has been investigated. The pH for Cr(III) ions adsorption was found as 3.0 for this material. The results showed that the adsorption equilibrium was reached after 45 min. The adsorption process is best described by the pseudo-second order kinetic model. Langmuir adsorption isotherm gave a satisfactory fit of the equilibrium data. The maximum adsorption capacity is ∼3 mg Cr(III) ions/g SIR. The thermodynamic studies allowed us to determine the thermodynamic parameters ΔG°, ΔH° and ΔS°. In this paper the factorial design of experiments was used to study the performance of the adsorption process.     

Inter-organizational innovation and cloud computing

Firms are increasingly shifting from the ‘closed’ innovation paradigm, in which their innovation design and implementation activities were based on their own internal knowledge resources, skills and production facilities, towards the inter-organizational ‘open’ innovation paradigm, which is based to a significant degree on collaboration with other organizations, aiming at the exploitation of external knowledge resources, skills and production facilities as well. This paper investigates empirically the effects of firm’s inter-organizational collaboration for the design and implementation of innovations, and also use of ICT for supporting this collaboration, on firm’s propensity to adopt cloud computing (CC), and in this way it examines in an ‘objective’ manner to what extent firms regard CC as a cost-effective means of supporting inter-organizational collaboration for the design and implementation of innovation. Our study is based on a dataset collected in the e-Business Survey of the European Commission from 676 European firms from the glass, ceramics and cement manufacturing sectors. It has been concluded that firms of these sectors regard CC as a cost-effective means of supporting collaboration with other firms for the design of innovations in their products, services and processes, and also of reducing the costs and increasing the capabilities and flexibility of already existing electronic support of inter-organizational innovation design collaboration. Furthermore, our results indicate that firms find CC useful for the reduction of the costs and the increase of the capabilities and flexibility of their existing electronic support of the complex operations required for the inter-organizational implementation of innovations.     

Malware clustering using suffix trees

Clustering is an important problem in malware research, as the number of malicious samples that appear every day makes manual analysis impractical. Although these samples belong to a limited number of malware families, it is difficult to categorize them automatically as obfuscation is involved. By extracting relevant features we can apply clustering algorithms, then only analyze a couple of representatives from each cluster. However, classic clustering algorithms that compute the similarity between each pair of samples are slow when a large collection is involved. In this paper, the features will be strings of operation codes extracted from the binary code of each sample. With a modified suffix tree data structure we can find long enough substrings that correspond to portions of a program’s code. These substrings must be filtered against a database of known substrings so that common library code will be ignored. The items that have common substrings above a certain threshold will be grouped into the same cluster. Our algorithm was tested with data extracted from real-world malware and constructed quality clusters.     

A new approach to predict ulcerative colitis activity through standard clinical–biological parameters using a robust neural network model

Neural Computing and Applications - Colonoscopy is the “gold” standard for evaluating disease activity in ulcerative colitis (UC). An important area of research is finding a...     

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.     

Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies

This article describes a framework for the deep and dynamic integration of learning strategies. The framework is based on the idea that each single-strategy learning method is ultimately the result of certain elementary inferences (like deduction, analogy, abduction, generalization, specialization, abstraction, concretion, etc.). Consequently, instead of integrating learning strategies at a macro level, we propose to integrate the different inference types that generate individual learning strategies. The article presents a concept-learning and theory-revision method that was developed in this framework. It allows the system to learn from one or from several (positive and/or negative) examples, and to both generalize and specialize its knowledge base. The method integrates deeply and dynamically different learning strategies, depending on the relationship between the input information and the knowledge base. It also behaves as a single-strategy learning method whenever the applicability conditions of such a method are satisfied.     

An empirical evaluation of P system testing techniques

This paper presents the existing techniques for P system testing and performs an empirical evaluation of their fault-detection efficiency. The comparison is performed using mutation testing and, based on the results obtained, some improved testing methodologies are proposed.     

Spatio‐temporal geometry fusion for multiple hybrid cameras using moving least squares surfaces

Multi‐view reconstruction aims at computing the geometry of a scene observed by a set of cameras. Accurate 3D reconstruction of dynamic scenes is a key component for a large variety of applications, ranging from special effects to telepresence and medical imaging. In this paper we propose a method based on Moving Least Squares surfaces which robustly and efficiently reconstructs dynamic scenes captured by a calibrated set of hybrid color+depth cameras. Our reconstruction provides spatio‐temporal consistency and seamlessly fuses color and geometric information. We illustrate our approach on a variety of real sequences and demonstrate that it favorably compares to state‐of‐the‐art methods.