University of New Mexico

About: University of New Mexico is a education organization based out in Albuquerque, New Mexico, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28870 authors who have published 64767 publications receiving 2578371 citations. The organization is also known as: UNM & Universitatis Novus Mexico.

Ionic polymer-metal composites: I. Fundamentals

Abstract: This paper, the first in a series of four review papers, presents a brief summary of the fundamental properties and characteristics of ionic polymeric-metal composites (IPMCs) as biomimetic sensors, actuators and artificial muscles. The forthcoming three review papers, to follow this paper, will address in detail such fundamentals and, in particular, manufacturing techniques and the electronic and electromechanical characteristics of IPMCs (part II), the phenomenological modelling of the underlying sensing and actuation mechanisms in IPMCs (part III) and the potential application areas for IPMCs (part IV). This paper is a summary of all recent findings and current state-of-the art manufacturing techniques, phenomenological laws and mechanical and electrical characteristics. A number of methodologies in developing high-force-density IPMCs are also reported.

Estimation of skeletal muscle mass by bioelectrical impedance analysis.

Abstract: The purpose of this study was to develop and cross-validate predictive equations for estimating skeletal muscle (SM) mass using bioelectrical impedance analysis (BIA). Whole body SM mass, determine...

A Baseline for the Multivariate Comparison of Resting-State Networks

Abstract: As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

Artificial Intelligence: Structures and Strategies for Complex Problem Solving

Abstract: From the Publisher: Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG. The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change. The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science. An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning. Includes new material on: Fundamentals of search, inference and knowledge representation AI algorithms and data structures in LISP and PROLOG Production systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systems. Machine-learning including ID3 with bagging and boosting, explanation based learning, PAC learning, and other forms of induction Neural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagation. Emergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial life. Object and agent-based problem solving and other forms of advanced knowledge representation