Dimensioning of Paper Machine Heat Recovery Recuperators

Designs of heat exchangers for heat recovery from humid air are described. A mathematical model for heat transfer with simultaneous mass transfer is constructed, based on heat and mass balances. Methods for solution are shown for two cases: 1. The water on the exhaust side of the heating surface is only condensate, whose heat content may be neglected. Also, the case of only partially condensing heat transfer surface is discussed. 2. The heating surface is flushed with abundant water. - Each case is illuminated with a calculation example.     

A local landscape mapping method for protein structure prediction in the HP model

The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction both for theoretical purposes and as a benchmark for new optimization strategies. In this work we present results of the recently proposed Hybrid Monte Carlo Ant Colony Optimization heuristic in the HP model using a fragment assembly-like strategy. Moreover we extend that method introducing a general framework for optimization in the HP model, called Local Landscape Mapping, and we test it using the pull moves set to generate solutions. We describe the heuristic and compare results obtained on well known HP instances in the 3-dimensional cubic lattice to those obtained with standard Ant Colony optimization and Simulated Annealing. Fragment assembly-like tests were performed using a modified objective function to prevent the creation of overlapping walks. Results show that our method performs better than the other heuristics in all benchmark instances when the fragment assembly-like strategy is used while in the case of pull moves-based neighborhood its performance is comparable to that of simulated annealing.     

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O₂ consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.     

Penetrating aortic ulcer: clinical and angiographic characteristics

BACKGROUND: "Penetrating aortic ulcer", an atherosclerotic lesion with ulceration that penetrates the internal elastic lamina and allows haematoma formation within the aortic wall, is rarely considered in the differential diagnosis of patients with sudden onset of severe chest or back pain. It has been suggested that it is a pathologic process that involves elderly hypertensive patients with severe atherosclerosis and rarely has been observed in the ascending aorta. METHODS: To determine the characteristics of this process, 11 clinical, 2 hemodynamic, 3 angiographic and 4 surgical variables were compared between 10 consecutive patients with penetrating aortic ulcers and 20 matched patients with classic acute aortic dissection. RESULTS: Clinical and hemodynamic variables were similar in both compared groups. In the group of patients with penetrating ulcer mean age was 58 +/- 6 years, previous hypertension was observed in 6 patients and the penetrating ulcer was located in the ascending aorta in 6 cases. In comparison to patients with aortic dissection, more angiographic projections were necessary to obtain the diagnosis in the group of patients with penetrating ulcer (2.4 +/- 0.8 vs 1.7 +/- 0.6; p < 0.05). In addition, the presence of angiographic aortic valve regurgitation was only observed in the group of patients with acute dissection (60% vs 0%; p < 0.001). Severe atherosclerosis was not present angiographically in any patient with penetrating ulcer. CONCLUSIONS: Penetrating aortic ulcer can also affect middle age patients without severe atherosclerosis and is frequently observed in the ascending aorta. Its form of presentation and clinical characteristics are similar to classic aortic dissection. The lack of angiographic confirmatory evidence of dissection with suggestive clinical history, should raise the possibility of penetrating aortic ulcer.     

Non-confluence in divisionless P systems with active membranes

We describe a solution to the SAT problem via non-confluent P systems with active membranes, without using membrane division rules. Furthermore, we provide an algorithm for simulating such devices on a nondeterministic Turing machine with a polynomial slowdown. Together, these results prove that the complexity class of problems solvable non-confluently and in polynomial time by this kind of P system is exactly the class .     

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg–Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN’s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.     

Hierarchies of parallel rewriting P systems — A survey

The paper is about some families of rewriting P systems, where the application of evolution rules is extended from the classical sequential rewriting to the parallel one (as, for instance, in Lindenmayer systems). As a result, consistency problems for the communication of strings may arise. Three variants of parallel rewriting P systems (already present in the literature) are considered here, together with the strategies they use to face the communication problem, and some parallelism methods for string rewriting are defined. We give a survey of all known results about each variant and we state some relations among the three variants, thus establishing hierarchies of parallel rewriting P systems. Various open problems related to the subject are also presented. Danicla Besozzi: She is assistant professor at the University of Milano. She received her M.S. in Mathematics (2000) from the University of Como and Ph.D. in Computer Science (2004) from the University of Milano. Her research interests cover topics in Formal Language Theory, Molecular Computing, Systems Biology. She is member of EATCS (European Association for Theoretical Computer Science) and EMCC (European Molecular Computing Consortium). Giancarlo Mauri: He is full professor of Computer Science at the University of Milano-Bicocca. His research interests are mainly in the area of theoretical computer science, and include: formal languages and automata, computational complexity, computational learning theory, soft computing techniques, cellular automata, bioinformatics and molecular computing. On these subjects, he published more than 150 scientific papers in international journals, contributed volumes and conference proceedings. Claudio Zandron: He received Ph.D. in Computer Science at the University of Milan, Italy, in 2001. Since 2002 he is assistant professor at the University of Milano-Bicocca, Italy. He is member of the EATCS (European Association for Theoretical Computer Science) and of EMCC (European Molecular Computing Consortium). His research interests are Molecular Computing (DNA and Membrane Computing) and Formal Languages.