Being your own role model for improving self‐efficacy: An elementary teacher self‐actualizes through drama‐based science teaching

Abstract:

Many teachers in elementary schools lack school science self‐efficacy, largely because of their inexperience with the subject. This frequently leads them to avoid teaching science or to teach it in ways that compromise the development of aspects of students’ scientific literacy. This paper describes how one teacher was able to improve her school science self‐efficacy through facilitated action research. In response to becoming aware of a discrepancy between her school science practices and her fundamental educational beliefs, Lisa developed a drama‐based, integrated science unit that she judged successful in helping students to achieve relevant learning goals. This experience led Lisa and her students to feel much more positive about teaching and learning in school science. Rather than learning from another, however, “Lisa, the science teacher” learned— to a great extent—from “Lisa, the drama‐based educator.” This finding has implications for science‐phobic teachers and for facilitators of their action research.     

Bioactive materials

Bioactive materials, including bioactive glasses, bioactive glass-ceramics, bioactive calcium phosphate ceramics and bioactive composites and coatings, bond to living tissues. The implant-tissue interfacial reactions and bonding mechanisms of the different bioactive materials are summarized. There are two types of bioactivities, osteoproductive bioactivity and osteoconductive bioactivity, due to different rates and mechanisms of implant-tissue interactions. To optimize the biochemical compatibility and biomechanical compatibility of the materials, two directions are proposed: (1) structural tailoring of bioactive composites and coatings and (2) molecular tailoring of surface chemistry.     

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.     

Dietary fats containing concentrates ofcis ortrans octadecenoates and the patterns of polyunsaturated fatty acids of liver phosphatidylcholine and phosphatidylethanolamine

The effects of the mixedcis- 18∶1 isomers and mixedtrans-18∶1 isomers present in partially hydrogenated soybean oil (PHSO) upon the patterns of polyunsaturated fatty acids (PUFA) in liver phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were studied in rats fed concentrates ofcis- 18∶1 ortrans- 18∶1 isomers isolated as triacylglycerides from PHSO. Thecis- 18∶1 andtrans- 18∶1 concentrates were fed at levels equal to those present in PHSO fed at 17.9% of the diet. All diets contained the required amounts of both linoleic and linolenic acids. Thetrans- 18∶1 concentrate was found to suppress the levels of 20∶4ω6 and 20∶3ω9, and to increase the levels of 18∶2ω6 and 20∶5ω3 in PC and PE. Thecis- 18∶1 concentrate suppressed 20∶4ω6 in PC, 20∶5ω3 in PC and PE, and 18∶2ω6 was more effective than thetrans concentrate in suppressing 22∶6ω3. Thetrans- 18∶1 concentrate was more effective in suppressing 20∶4ω6. Thetrans-18∶ isomers appear to modify PUFA metabolism by inhibition of PUFA synthesis, whereas thecis- 18∶1isomers appear to compete with 2-position fatty acyl transfer and to inhibit ω3 PUFA acylation.     

Biological Wastewater Treatment System Design Part II. Effect of Parameter Variations on Optimal Process System Structure and Design

This paper presents a study of the effects of parameter variations on optimal process system structures and designs for biological wastewater treatment systems. Results indicate that optimal system structure and design are sensitive to variations in the rate constants and feed concentration. Some of the process flowsheets deduced are innovative and have surprisingly low total costs. The process system structures and designs developed in this study merit consideration in future planning and design of wastewater treatment systems.     

Dynamic modeling of compliant constant-force compression mechanisms

A mathematical dynamic model is derived for compliant, constant-force compression mechanisms, based on the pseudo-rigid-body model simplification of the device. The compliant constant-force compression mechanism (CFCM) is a slider mechanism incorporating large-deflection beams, which outputs near-constant-force across the range of its designed deflection. The equation of motion is successfully calibrated with empirical data from five separate mechanisms, comprising two basic configurations of CFCMs. The dynamic equation is derived from a generalized pseudo-rigid-body model. This allows every configuration to be represented by the same model, so a separate treatment is not required for each configuration. An unexpected dynamic trait of the constant-force mechanism is discovered. There exists a range of input deflection frequencies for which the output force of the mechanism is nearer to constant-force than it is with static input deflections.     

Efficient parallel algorithms for graph problems

We present an efficient technique for parallel manipulation of data structures that avoids memory access conflicts. That is, this technique works on the Exclusive Read/Exclusive Write (EREW) model of computation, which is the weakest shared memory, MIMD machine model. It is used in a new parallel radix sort algorithm that is optimal for keys whose values are over a small range. Using the radix sort and known results for parallel prefix on linked lists, we develop parallel algorithms that efficiently solve various computations on trees and unicycular graphs. Finally, we develop parallel algorithms for connected components, spanning trees, minimum spanning trees, and other graph problems. All of the graph algorithms achieve linear speedup for all but the sparsest graphs.Part of this work was done while the first author was at the University of Illinois, Urbana-Champaign, the second author was at Carnegie-Mellon University, and the third author was at the Hebrew University and the Courant Institute of Mathematical Sciences, New York University. A preliminary version of this work was presented at the 1986 International Conference on Parallel Processing.     

Empirical Learning as a Function of Concept Character

Concept learning depends on data character. To discover how, some researchers have used theoretical analysis to relate the behavior of idealized learning algorithms to classes of concepts. Others have developed pragmatic measures that relate the behavior of empirical systems such as ID3 and PLS1 to the kinds of concepts encountered in practice. But before learning behavior can be predicted, concepts and data must be characterized. Data characteristics include their number, error, size, and so forth. Although potential characteristics are numerous, they are constrained by the way one views concepts. Viewing concepts asfunctions over instance space leads to geometric characteristics such as concept size (the proportion of positive instances) and concentration (not too many peaks). Experiments show that some of these characteristics drastically affect the accuracy of concept learning. Sometimes data characteristics interact in non-intuitive ways; for example, noisy data may degrade accuracy differently depending on the size of the concept. Compared with effects of some data characteristics, the choice of learning algorithm appears less important: performance accuracy is degraded only slightly when the splitting criterion is replaced with random selection. Analyzing such observations suggests directions for concept learning research.     

Groundwater Resources Sustainability: Past, Present, and Future

With the worldwide depletion of groundwater and the intensified use around the world, particularly in many arid and semi-arid regions for irrigation and municipal use, there is no satisfactory approach to groundwater sustainability. The lack of and miss-management of this valuable resource has not only created serious groundwater pollution problems but has created present and/or future water supply problems. This paper does not present a solution, but instead examines economic ideas such as exhaustible resource theory (over exploitation), and optimization methodologies that can incorporate new ideas of groundwater sustainability, population growth constraints, include both short term and long term consequences, and consider multi-objectives. Concepts of groundwater footprint, recharge, and safe yield are discarded as concepts for measuring groundwater sustainability. The concept of developing a sustainability index that could also be used within the context of optimization is introduced. Also the concepts of traditional knowledge are discussed with the emphasis on the use of these methodologies for both developed and developing regions of the world to achieve groundwater sustainability.     

Class consistent k-means: Application to face and action recognition

A class-consistent k-means clustering algorithm (CCKM) and its hierarchical extension (Hierarchical CCKM) are presented for generating discriminative visual words for recognition problems. In addition to using the labels of training data themselves, we associate a class label with each cluster center to enforce discriminability in the resulting visual words. Our algorithms encourage data points from the same class to be assigned to the same visual word, and those from different classes to be assigned to different visual words. More specifically, we introduce a class consistency term in the clustering process which penalizes assignment of data points from different classes to the same cluster. The optimization process is efficient and bounded by the complexity of k-means clustering. A very efficient and discriminative tree classifier can be learned for various recognition tasks via the Hierarchical CCKM. The effectiveness of the proposed algorithms is validated on two public face datasets and four benchmark action datasets.