Cover Image,Volume 98, Issue 10

The cover image, by Vessela Kancheva et al., is based on the Research Article Protective effects of new antioxidant compositions of 4‐methylcoumarins andrelated compounds with DL‐α‐tocopherol and L‐ascorbic acid, DOI: 10.1002/jsfa.8892 .

     

Energy-optimal electrical excitation of nerve fibers

We derive, based on an analytical nerve membrane model and optimal control theory of dynamical systems, an energy-optimal stimulation current waveform for electrical excitation of nerve fibers. Optimal stimulation waveforms for nonleaky and leaky membranes are calculated. The case with a leaky membrane is a realistic case. Finally, we compare the waveforms and energies necessary for excitation of a leaky membrane in the case where the stimulation waveform is a square-wave current pulse, and in the case of energy-optimal stimulation. The optimal stimulation waveform is an exponentially rising waveform and necessitates considerably less energy to excite the nerve than a square-wave pulse (especially true for larger pulse durations). The described theoretical results can lead to drastically increased battery lifetime and/or decreased energy transmission requirements for implanted biomedical systems.     

Inductive learning in deductive databases

Most current applications of inductive learning in databases take place in the context of a single extensional relation. The authors place inductive learning in the context of a set of relations defined either extensionally or intentionally in the framework of deductive databases. LINUS, an inductive logic programming system that induces virtual relations from example positive and negative tuples and already defined relations in a deductive database, is presented. Based on the idea of transforming the problem of learning relations to attribute-value form, several attribute-value learning systems are incorporated. As the latter handle noisy data successfully, LINUS is able to learn relations from real-life noisy databases. The use of LINUS for learning virtual relations is illustrated, and a study of its performance on noisy data is presented     

Acquiring background knowledge for machine learning using function decomposition: a case study in rheumatology

Domain or background knowledge is often needed in order to solve difficult problems of learning medical diagnostic rules. Earlier experiments have demonstrated the utility of background knowledge when learning rules for early diagnosis of rheumatic diseases. A particular form of background knowledge comprising typical co-occurrences of several groups of attributes was provided by a medical expert. This paper explores the possibility of automating the process of acquiring background knowledge of this kind and studies the utility of such methods in the problem domain of rheumatic diseases. A method based on function decomposition is proposed that identifies typical co-occurrences for a given set of attributes. The method is evaluated by comparing the typical co-occurrences it identifies as well as their contribution to the performance of machine learning algorithms, to the ones provided by a medical expert.     

A reappraisal of saprobic values and indicator weights based on slovenian river quality data

The saprobic values and indicator weights used in the Slovenian saprobic system are reappraised using data from the 1990 to 95 river quality surveys of Slovenia. The conceptual basis of the reappraisal is described and then formulated mathematically. The analysis is based on 1106 biological samples and covers 300 taxa. The results are expressed in terms of revised saprobic values and indicator weights that mirror the ones previously assigned by ecological experts. The most significant differences between original and revised values are highlighted and discussed. It is concluded that: (a) the revised values and weights are more representative of their ‘true’ values than are the original values and weights, but that it would be premature to consider them definitive; (b) the analytical method provides a sound data-based approach to the revision of saprobic values and indicator weights; and (c) the method could help to improve and harmonise the various saprobic systems currently in use across Europe.     

In Vitro Interaction of 5-Aminoorotic Acid and Its Gallium(III) Complex with Superoxide Radical,Generated by Two Model Systems

Increased levels of the superoxide radical are associated with oxidative damage to healthy tissues and with elimination of malignant cells in a living body. It is desirable that a chemotherapeutic combines pro-oxidant behavior around and inside tumors with antioxidant action near healthy cells. A complex consisting of a pro-oxidant cation and antioxidant ligands could be a potential anticancer agent. Ga(III) salts are known anticancer substances, and 5-aminoorotic acid (HAOA) is a ligand with antioxidant properties. The in vitro effects of HAOA and its complex with Ga(III) (gallium(III) 5-aminoorotate (GaAOA)) on the in vitro accumulation of superoxide and other free radicals were estimated. Model systems such as potassium superoxide (KO₂), xanthine/xanthine oxidase (X/XO), and rat blood serum were utilized. Data suggested better antioxidant effect of GaAOA compared to HAOA. Evidently, all three ligands of GaAOA participated in the scavenging of superoxide. The effects in rat blood serum were more nuanced, considering the chemical and biochemical complexity of this model system. It was observed that the free-radical-scavenging action of both compounds investigated may be manifested via both hydrogen donation and electron transfer pathways. It was proposed that the radical-scavenging activities (RSAs) of HAOA and its complex with Ga(III) may be due to a complex process, depending on the concentration, and on the environment, nature, and size of the free radical. The electron transfer pathway was considered as more probable in comparison to hydrogen donation in the scavenging of superoxide by 5-aminoorotic acid and its gallium(III) complex.     

Extinction of counterfiow diffusion flames with halon replacements

Extinction of counter/low diffusion flames on liquid fuels was investigated, to confirm the superiority of the counterflow diffusion flame over the cup burner method for measuring flame extinguishing concentrations of fire suppressants, and to examine the fire suppression effects of halon replacements. The flame extinguishing concentration for the counterflow flame was less sensitive to the burner size than that for the cup burner method. Furthermore, the flow velocity of the fuel vapor had no change when the suppressant concentration in the oxidizer mixture of the counterflow diffusion flame was varied, whereas it changed remarkably in the case of the cup burner flame. The flame extinguishing concentrations of nitrogen, carbon dioxide, halon 1301 (CF₃Br), and three kinds of hydrofluorocarbons (HFC) and perfluorocarbon (FC) for n-heptane or ethanol counterflow flames were measured at various strain rates. Adiabatic flame temperatures at the extinction concentrations were calculated using the flame extinguishing concentrations measured for counterflow flames, assuming various equivalence ratios. The results suggest that HFC-23 (CHF₃) suppression exhibits a higher contribution to the chemical suppression effect than other HFC or FC.     

If approximating nonlinear areas, then consider fuzzy systems

The fuzzy system is a knowledge-based system consisting of linguistic if-then rules. This article will focus on the Takagi-Sugeno (TS) type models. A novel approach of INFUMO has been applied in fault detection. The INFUMO was derived using the l_infin-norm function approximation. The majority of papers focus on the construction of the so-called residual generator, a comparator of the process and the process-model output that creates a residual signal