Geographical variation in bill size across bird species provides evidence for Allen's rule

Allen’s rule proposes that the appendages of endotherms are smaller, relative to body size, in colder climates, in order to reduce heat loss. Empirical support for Allen’s rule is mainly derived from occasional reports of geographical clines in extremity size of individual species. Interspecific evidence is restricted to two studies of leg proportions in seabirds and shorebirds. We used phylogenetic comparative analyses of 214 bird species to examine whether bird bills, significant sites of heat exchange, conform to Allen’s rule. The species comprised eight diverse taxonomic groups—toucans, African barbets, Australian parrots, estrildid finches, Canadian galliforms, penguins, gulls, and terns. Across all species, there were strongly significant relationships between bill length and both latitude and environmental temperature, with species in colder climates having significantly shorter bills. Patterns supporting Allen’s rule in relation to latitudinal or altitudinal distribution held within all groups except the finches. Evidence for a direct association with temperature was found within four groups (parrots, galliforms, penguins, and gulls). Support for Allen’s rule in leg elements was weaker, suggesting that bird bills may be more susceptible to thermoregulatory constraints generally. Our results provide the strongest comparative support yet published for Allen’s rule and demonstrate that thermoregulation has been an important factor in shaping the evolution of bird bills.

Hierarchical rule generalisation for speaker identification in fiction books

This paper presents a hierarchical pattern matching and generalisation technique which is applied to the problem of locating the correct speaker of quoted speech found in fiction books. Patterns from a training set are generalised to create a small number of rules, which can be used to identify items of interest within the text. The pattern matching technique is applied to finding the Speech-Verb, Actor and Speaker of quotes found in ction books. The technique performs well over the training data, resulting in rule-sets many times smaller than the training set, but providing very high accuracy. While the rule-set generalised from one book is less effective when applied to different books than an approach based on hand coded heuristics, performance is comparable when testing on data closely related to the training set.

On 4 June 2008 Siberian Red Knots at Elbe Mouth kissed the canonical evening migration departure rule goodbye

Observations of departing Siberian-breeding Red Knots Calidris canutus canutus from their central staging site during northward migration, the Schleswig-Holstein Wadden Sea, Germany, in early June 2008, challenge the established notion that departing long-distance migrating waders only leave around sunset. During four days we scanned several thousand Red Knots for colour-ringed individuals and found a total of 20 different individuals that were previously ringed at either their main wintering site, the Banc d'Arguin in Mauritania, or at stopover sites on the Atlantic coast of France. Body masses of captured Red Knots in Schleswig-Holstein were higher than 200 g and hematocrite values showed an average of 58%, clearly indicating that they were ready for take-off. On all except one evening, we noted impressive departure movements during the incoming tide. On that exceptional evening a cold front thunderstorm passed over the area. Late the next morning, thousands of Red Knots departed during the incoming tide. We assume that the birds avoided taking off in adverse weather conditions and elaborate why Red Knots presumably traded off advantages from departing during twilight. We suggest that during spring migration, schedules are so tight that further delays decrease fitness, either because it would cause another full day of exposure to high predation risk by falcons, or because of conditions upon arrival on the tundra.

A guided rule reduction system for prioritization of failures in fuzzy FMEA

Traditional Failure Mode and Effect Analysis (FMEA) utilizes the Risk Priority Number (RPN) ranking system to evaluate the risk level of failures, to rank failures, and to prioritize actions. Although this method is simple, it suffers from several shortcomings. In this paper, use of fuzzy inference techniques for RPN determination in an attempt to overcome the weaknesses associated with the traditional RPN ranking system is investigated. However, the fuzzy RPN model, suffers from the combinatorial rule explosion problem. As a result, a generic rule reduction approach, i.e. the Guided Rule Reduction System (GRRS), is proposed to reduce the number of rules that need to be provided by users during the fuzzy RPN modeling process. The proposed approach is evaluated using real-world case studies pertaining to semiconductor manufacturing. The results are analyzed, and implications of the proposed approach are discussed.

A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ??don't care?? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.

On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models

Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A mathematical derivation on the conditions of an FIS to be monotone is provided. From the derivation, two conditions are necessary. The derivation suggests that the mapped consequence fuzzy set of an FIS to be of a monotonicity order. We further evaluate the use of fuzzy rule interpolation techniques in predicting a consequent associated with an observation according to the monotonicity order. There are several findings in this article. We point out the importance of an ordering criterion in rule selection for a multi-input FIS before the interpolation process; and hence, the practice of choosing the nearest rules may not be true in this case. To fulfill the monotonicity order, we argue with an example that conventional fuzzy rule interpolation techniques that predict each consequence separately is not suitable in this case. We further suggest another class of interpolation techniques that predicts the consequence of a set of observations simultaneously, instead of separately. This can be accomplished with the use of a search algorithm, such as the brute force, genetic algorithm or etc.

Fuzzy rule base assessment models : theoretical analyses and a case study

An assessment model is usually a mathematical model that produces a measuring index, in the form of a numerical score to a situation/object, with respect to the subject of measure. To allow a valid and useful comparison among various situations/objects according to their associated numerical scores to be made, two important properties, i.e., the monotone output property and output resolution properties, are essential in fuzzy inference-based assessment problems. In this paper, the conditions for a fuzzy assessment model to fulfill the monotone output property is investigated using a derivative approach. A guideline on how the input membership functions should be tuned is also provided. Besides, the output resolution property is defined as the derivative of the output of the assessment model with respect to the input, whereby the derivative should be greater than a minimum resolution. Based on the derivative, improvements to the output resolution property by refining the fuzzy production rules are suggested. A case study on the Bowles fuzzy RPN model to demonstrate the effectiveness of the properties is also included.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.