Construction of aggregation functions from data using linear programming

This article examines the construction of aggregation functions from data by minimizing the least absolute deviation criterion. We formulate various instances of such problems as linear programming problems. We consider the cases in which the data are provided as intervals, and the outputs ordering needs to be preserved, and show that linear programming formulation is valid for such cases. This feature is very valuable in practice, since the standard simplex method can be used.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN) that combines the Fuzzy ARTMAP (FAM) network and a Hybrid Evolutionary Programming (HEP) model is introduced. The proposed FAM-HEP model, which combines the strengths of FAM and HEP, is able to construct its network structure autonomously as well as to perform learning and evolutionary search and adaptation concurrently. The effectiveness of the proposed FAM-HEP network is assessed empirically using several benchmark data sets and a real medical diagnosis problem. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP, FAM, and other classification models. In general, the results of FAM-HEP are better than those of FAM-EP and FAM, and are comparable with those from other classification models. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems in general, and a promising computerized decision support tool for tackling medical diagnosis tasks in particular.

The use of laptop computers in programming lectures

This research explores the effect of the use of laptop computers on students’ learning experiences during lectures. Our methodology involves embedding laptops with visualization software as a learning aid during lectures. We then employ a framework of seven principles of good practice in higher education to evaluate the impact of the use of laptop computers on the learning experience of computer programming students. Overall, we found that students were highly motivated and supportive of this innovative use of laptop computers with lectures.

Contribution of stretch to the change of activation properties of muscle fibers in the diaphragm at the transition from fetal to neonatal life

The transition from fetal to postnatal life involves clearance of liquid from the lung and airways, and rapid formation of a functional residual capacity. Despite the importance of the diaphragm in this process, the impact of birth on the mechanical and functional activity of its muscle fibers is not known. This study determined the contractile characteristics of individual “skinned” diaphragm fibers from 70 days (0.47) gestation to after birth in sheep. Based on differential sensitivity to the divalent ions calcium (Ca²⁺) and strontium (Sr²⁺), all fibers in the fetal diaphragm were classified as “fast,” whereas fibers from the adult sheep diaphragm exhibited a “hybrid” phenotype where both “fast” and “slow” characteristics were present within each single fiber. Transition to the hybrid phenotype occurred at birth, was evident after only 40 min of spontaneous breathing, and could be induced by simple mechanical stretch of diaphragm fibers from near-term fetuses (∼147 days gestation). Both physical stretch of isolated fibers, and mechanical ventilation of the fetal diaphragm in situ, significantly increased sensitivity to Ca²⁺ and Sr²⁺, maximum force generating capacity, and decreased passive tension in near-term and preterm fetuses; however, only fibers from near-term fetuses showed a complete transition to a “hybrid” activation profile. These findings suggest that stretch associated with the transition from a liquid to air-filled lung at birth induces physical changes of proteins determining the activation and elastic properties of the diaphragm. These changes may allow the diaphragm to meet the increased mechanical demands of breathing immediately after birth.

Developmental origins of obesity-related hypertension

1. In the past 30 years the prevalence of obesity and overweight have doubled. It is now estimated that globally over 500 million adults are obese and a further billion adults are overweight. Obesity is a cardiovascular risk factor and some studies suggest that up to 70% of cases of essential hypertension may be attributable, in part, to obesity. Increasingly, evidence supports a view that obesity-related hypertension may be driven by altered hypothalamic signalling, which results in inappropriately high appetite and sympathetic nerve activity to the kidney.

2. In addition to the adult risk factors for obesity and hypertension, the environment encountered in early life may ‘programme’ the development of obesity, hypertension and cardiovascular disease. In particular, maternal obesity or high dietary fat intake in pregnancy may induce changes in fetal growth trajectories and predispose individuals to develop obesity and related sequelae.

3. The mechanisms underlying the programming of obesity-related hypertension are becoming better understood. However, several issues require clarification, particularly with regard to the role of the placenta in transferring fatty acid to the fetal compartment, the impact of placental inflammation and cytokine production in obesity.

4. By understanding which factors are most associated with the development of obesity and hypertension in the offspring, we can focus therapeutic and behavioural interventions to most efficiently reduce the intergenerational propagation of the obesity cycle.

Using linear programming for weights identification of generalized bonferroni means in R

The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset.