Ultrafiltration of protein solutions; the role of protein association in rejection and osmotic pressure

The monomer-dimer equilibrium of the protein β-lactoglobulin under neutral conditions appears to influence the rejection and the osmotic pressure build-up, both phenomena closely related to ultrafiltration. Rejection measurements indicate different rejections for the β-lactoglobulin monomers and dimers: the membrane rejects the dimer almost completely and the monomer only partially. The osmotic pressure turns out to be highly dependent on the protein concentration. A good agreement, up to high concentrations, is found between experimental data and theoretical osmotic pressures, calculated by taking into account the state of association, the excluded volume and the Donnan effects. The effect of changes in pH on the osmotic pressure has been measured: a minimum was found around pH = 4.5, where according to the literature, maximum protein-protein interaction occurs

An analysis of various elastic net algorithms

The Elastic Net Algorithm (ENA) for solving the Traveling Salesman Problem is analyzed applying statistical mechanics. Using some general properties of the free energy function of stochastic Hopfield Neural Networks, we argue why Simic's derivation of the ENA from a Hopfield network is incorrect. However, like the Hopfield-Lagrange method, the ENA may be considered a specific dynamic penalty method, where, in this case, the weights of the various penalty terms decrease during execution of the algorithm. This view on the ENA corresponds to the view resulting from the theory on `deformable templates', where the term stochastic penalty method seems to be most appropriate. Next, the ENA is analyzed both on the level of the energy function as well as on the level of the motion equations. It will be proven and shown experimentally, why a non-feasible solution is sometimes found. It can be caused either by a too rapid lowering of the temperature parameter (which is avoidable), or by a peculiar property of the algorithm,namely, that of adhering to equidistance of the elastic net points. Thereupon, an alternative, Non-equidistant Elastic Net Algorithm (NENA) is presented and analyzed. It has a correct distance measure and it is hoped to guarantee feasibility in a more natural way. For small problem instances, this conjecture is confirmed experimentally. However, trying larger problem instances, the pictures changes: our experimental results show that the elastic net points appear to become `lumpy' which may cause non-feasibility again. Moreover, in cases both algorithms yield a feasible solution, the quality of the solution found by the NENA is often slightly worse than the one found by the original ENA. This motivated us to try an Hybrid Elastic Net Algorithm (HENA), which starts using the ENA and, after having found a good approximate solution, switches to the NENA in order to guarantee feasibility too. In practice, the ENA and HENA perform more or less the same. Up till now, we did not find parameters of the HENA, which invariably guarantee the desired feasibility of solutions

Generalized Hopfield networks for constrained optimization

A twofold generalization of the classical continuous Hopfield neural network for modelling constrained optimization problems is proposed. On the one hand, non-quadratic cost functions are admitted corresponding to non-linear output summation functions in the neurons. On the other hand it is shown under which conditions various (new) types of constraints can be incorporated directly. The stability properties of several relaxation schemes are shown. If a direct incorporation of the constraints appears to be impossible, the Hopfield-Lagrange model can be applied, the stability properties of which are analyzed as well. Another good way to deal with constraints is by means of dynamic penalty terms, using mean field annealing in order to end up in a feasible solution. A famous example in this context is the elastic net, although it seems impossible - contrary to what is suggested in the literature - to derive the architecture of this network from a constrained Hopfield model. Furthermore, a non-equidistant elastic net is proposed and its stability properties are compared to those of the classical elastic network. In addition to certain simulation results as known from the literature, most theoretical statements of this paper are validated with simulations of toy problems while in some cases, more sophisticated combinatorial optimization problems have been tried as well. In the final section, we discuss the possibilities of applying the various models in the area of constrained optimization. It is also demonstrated how the new ideas as inspired by the analysis of generalized continuous Hopfield models, can be transferred to discrete stochastic Hopfield models. By doing so, simulating annealing can be exploited in other to improve the quality of solutions. The transfer also opens new avenues for continued theoretical research

Business cycles and compositional variation in U.S. unemployment

In the past decades several features of U.S. unemployment dynamics have been investigated empirically. The original focus of research was onthe duration of unemployment. In later studies the cyclicality of incidence and duration, compositional effects and duration dependence of the exitrate out of unemployment have been investigated. Unlike the partial approach of previous studies this paper takes all elements of unemployment dynamics simultaneously into account. We find that cyclical fluctuations in unemployment are driven by variations in the incidence, individual exit probabilities and the composition of the inflow into unemployment. We also find negative duration dependence of the unemployment exit rate which can be attributed to employers ranking workers according to the length of their unemployment spell

The Non-Parametric Identification of the Mixed Proportional Hazards Competing Risks Model

We prove identification of dependent competing risks models in whicheach risk has a mixed proportional hazard specification with regressors, and the risks are dependent by way of the unobserved heterogeneity, or frailty, components. We show that the conditions for non-parametric identification given by Heckman and Honoré (1989) can be relaxed. We generalize the results for the case in which multiple spells are observed for each subject

Internetbeveiliging: een beheerperspektief

Binnen het generieke kader van het beheer van informatiesystemen wordt de beveiligingsproblematiek rond het gebruik van het Internet onder de loep genomen en geanalyseerd. De invalshoek is zowel technisch als organisatorisch. Na een korte analyse van het nut van computernetwerken voor een organisatie worden - aan de hand van een basaal communicatiemodel - de potentiele risico's van Internetgebruik in kaart gebracht. Op basis hiervan kan een (voor de specifieke organisatie) noodzakelijk beveiligingsnivo worden gedefinieerd. Na vaststelling hiervan dienen bijpassende technische en organisatorische maatregelen te worden genomen. Deze (noodgedwongen zeer dynamische) aanpak wordt geformaliseerd met behulp van een struktuurmodel. In aparte kaders worden zowel de onderscheiden typen risico's als de onderscheiden soorten maatregelen nader toegelicht aan de hand van allerlei praktijkvoorbeelden

Competitive exception learning using fuzzy frequency distributions

A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy frequency distribution describing the general, average system's output behavior. Next, we look for a fuzzy partitioning of the input space in such away that the corresponding fuzzy output frequency distributions `deviate at most' from the average one as found in the first step. In this way, the most important `exceptional regions' in the input-output relation are determined. Using the joint input-output fuzzy frequency distributions, the complete input-output function as extracted from the data, can be expressed mathematically. In addition, the exceptions encountered can be collected and described as a set of fuzzy if-then-else-rules. Besides presenting a theoretical description of the new exception learning algorithm, we report on the outcomes of certain practical simulations

Associative conceptual space-based information retrieval systems

In this `Information Era' with the availability of large collections of books, articles, journals, CD-ROMs, video films and so on, there exists an increasing need for intelligent information retrieval systems that enable users to find the information desired easily. Many attempts have been made to construct such retrieval systems, including the electronic ones used in libraries and including the search engines for the World Wide Web. In many cases, however, the so-called `precision' and `recall' of these systems leave much to be desired. In this paper, a new AI-based retrieval system is proposed, inspired by, among other things, the WEBSOM-algorithm. However, contrary to that approach where domain knowledge is extracted from the full text of all books, we propose a system where certain specific meta-information is automatically assembled using only the index of every document. This knowledge extraction process results into a new type of concept space, the so-called Associative Conceptual Space where the `concepts' as found in all documents are clustered using a Hebbian-type of learning algorithm. Then, each document can be characterised by comparing the concepts as occurring in it to those present in the associative conceptual space. Applying these characterisations, all documents can be clustered such that semantically similar documents lie close together on a Self-Organising Map. This map can easily be inspected by its user