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Books abound to guide mentoring and coaching for preservice and inservice teachers' professional learning. However, none fully account for the differences among teachers in experience and expertise and how these factors change over time. This book addresses this need by presenting a dynamic model for teacher/coach interactions, the Gradual Increase of Responsibility (GIR) model for mentoring and coaching. Like students, teachers benefit when support is personalized. The GIR model includes five coaching moves that are selectively used to match support to need. This book guides mentors and coaches in refining their approaches, helping them provide differentiated support to teachers from a range of grade levels, academic areas, contexts, and levels of experience. As strains on teachers escalate, mentoring and coaching using the GIR model is an effective, energizing approach to prepare, sustain, and retain teachers and increase their instructional effectiveness. This book features: (1) A conceptually simple model that expands upon personalized development in the relational work of coaching; (2) Five coaching interactions that vary incrementally in level of support; (3) Callout boxes with helpful questions, lists, and procedures, plus online resources for additional support; (4) Examples that illustrate how to use the GIR model with teachers from preservice to the expert practitioner; and (5) "Reflect and Respond" prompts in each chapter that encourage contemplation of the concepts and support application.

Discover how Lesson Study benefits both students and teachers. Unlike scripted curricula that strip teachers of professional decisionmaking, Lesson Study values teachers by expecting them to be agents of improvement in their own classrooms. This resource empowers readers to oppose reform efforts that minimize teacher agency by offering an evidence-based approach to teacher-led instructional improvement. The text provides structures for attending to students' interests, knowledge, and values when planning, teaching, reflecting, and revising instruction. It also shows educators how to use Lesson Study to design culturally responsive, differentiated instruction for the K-12 classroom. Use this step-by-step guide to develop professional learning communities; increase teacher motivation, efficacy, and knowledge; and support improvement adapted to local contexts. This book: (1) Guides readers through three cycles of Lesson Study, taking teacher learning deeper with each cycle; (2) Focuses on developing student understanding that supports meaningful instruction across academic areas; (3) Emphasizes the utility of Lesson Study for informing culturally responsive instruction; (4) Offers examples from a variety of grade-levels and content areas, featuring both pre- and inservice teachers; and (5) Includes additional resources and prompts in each chapter to guide application. [Foreword by Ellin Oliver Keene.]

Control of membrane voltage and membrane current measurements are of strong interest for the study of numerous aspects of skeletal muscle physiology and pathophysiology. The silicone-clamp technique makes use of a conventional patch-clamp apparatus to achieve whole-cell voltage clamp of a restricted portion of a fully differentiated adult skeletal muscle fiber. The major part of an isolated muscle fiber is insulated from the extracellular medium with silicone grease, and the tip of a single microelectrode connected to the amplifier is then inserted within the fiber through the silicone layer. This method represents an alternative to the traditional vaseline-gap isolation and two or three microelectrode voltage-clamp techniques. This chapter reviews the main benefits of the silicone-clamp technique and provides detailed insights into its practical implementation. [ABSTRACT FROM AUTHOR]

Randomization is an efficient tool for global optimization. We here define a method which keeps : - the order 0 of evolutionary algorithms (no gradient) ; - the stochastic aspect of evolutionary algorithms ; - the efficiency of so-called "low-dispersion" points ; and which ensures under mild assumptions global convergence with linear convergence rate. We use i) sampling on a ball instead of Gaussian sampling (in a way inspired by trust regions), ii) an original rule for step-size adaptation ; iii) quasi-monte-carlo sampling (low dispersion points) instead of Monte-Carlo sampling. We prove in this framework linear convergence rates i) for global optimization and not only local optimization ; ii) under very mild assumptions on the regularity of the function (existence of derivatives is not required). Though the main scope of this paper is theoretical, numerical experiments are made to backup the mathematical results. [ABSTRACT FROM AUTHOR]

Numerical optimization of given objective functions is a crucial task in many real-life problems. The present article introduces an immunological algorithm for continuous global optimization problems, called opt-IA. Several biologically inspired algorithms have been designed during the last few years and have shown to have very good performance on standard test bed for numerical optimization. In this paper we assess and evaluate the performance of opt-IA, FEP, IFEP, DIRECT, CEP, PSO, and EO with respect to their general applicability as numerical optimization algorithms. The experimental protocol has been performed on a suite of 23 widely used benchmarks problems. The experimental results show that opt-IA is a suitable numerical optimization technique that, in terms of accuracy, generally outperforms the other algorithms analyzed in this comparative study. The opt-IA is also shown to be able to solve large-scale problems. Keywords: Artificial Immune Systems, Clonal Selection Algorithms, Immune Algorithm, Aging operator, Global Numerical Optimization. [ABSTRACT FROM AUTHOR]

Stochastic local search (SLS) methods like evolutionary algorithms, ant colony optimisation or iterated local search receive an ever increasing attention for the solution of highly application relevant optimisation problems. Despite their noteworthy successes, several issues still hinder their even wider spread. One central issue is the configuration and parameterisation of SLS methods, which is known to be a time- and personal-intensive process. Recently, several attempts have been made to automate the tuning of SLS algorithms. One of the most promising directions is the usage of the racing methodology, which is a statistical method for selecting promising candidate configurations. We present results of a study on the application of this methodology to the tuning of a complex SLS method for an industrial vehicle scheduling and routing problem, and compare the performance of two racing methods. [ABSTRACT FROM AUTHOR]

Recently, an electromagnetism (EM) heuristic has been introduced by Birbil and Fang (2003) to solve unconstrained optimization problems. In this paper, we extend the EM methodology to combinatorial optimization problems and illustrate its effectiveness on the well-known resource-constrained project scheduling problem (RCPSP). We present computational experiments on a standard benchmark dataset, compare the results of the different modifications on the original EM framework with current state-of-the-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the problem under study. We also give directions for future research in order to further explore the potential of this new technique. [ABSTRACT FROM AUTHOR]

An encoding scheme for protein folding on lattice models, inspired by parametric L-systems, was proposed. The encoding incorporates problem domain knowledge in the form of predesigned production rules that capture commonly known secondary structures: α-helices and β-sheets. The ability of this encoding to capture protein native conformations was tested using an evolutionary algorithm as the inference procedure for discovering L-systems. Results confirmed the suitability of the proposed representation. It appears that the occurrence of motifs and sub-structures is an important component in protein folding, and these sub-structures may be captured by a grammar-based encoding. This line of research suggests novel and compact encoding schemes for protein folding that may have practical implications in solving meaningful problems in biotechnology such as structure prediction and protein folding. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced genetic algorithm for the protein structure prediction problem. A new fitness function, that uses the concept of radius of gyration, is proposed. Also, a novel operator called partial optimization, together with different strategies for performance improvement, are described. Tests were done with five different amino acid chains from 20 to 85 residues long and better results were obtained, when compared with those in the current literature. Results are promising and suggest the suitability of the proposed method for protein structure prediction using the 2D HP model. Further experiments shall be done with longer amino acid chains as well as with real-world proteins. [ABSTRACT FROM AUTHOR]

In the research field of Artificial Life, the concepts of emergence and adaptation form the basis of a class of models which describes reproducing individuals whose characteristics evolve over time. These models allow to investigate the laws of evolution, to observe emergent phenomena at individual and population level, and additionally yield new design techniques for computer animation and robotics industries. This paper presents an introductory non-exhaustive survey of the constitutive work of the last twenty years. When examining the history of development of these models, different periods can be distinguished. Each one incorporated new modeling concepts, however to this day all the models have failed to exhibit long-lasting, let alone open-ended evolution. A particular look at the richness of dynamics of the modeled environments reveals that only little attention has been paid to their design, which could account for the experienced evolutionary barrier. [ABSTRACT FROM AUTHOR]

We investigate the effect that population structure has upon the course of artificial evolution. We represent an arbitrary population structure by embedding a population of individuals in a graph. Each individual resides at a vertex of the graph and can only choose a mating partner from among its neighbors in the graph. Each individual mates with the selected partner and is replaced by the resultant offspring in the next generation. We embed populations in a variety of trees and mesh-structured graphs and observe differences in rates of change of average fitness and percent polymorphism over successive generations. Results indicate that populations embedded in sparse random graphs having relatively low diameter yield results similar to those embedded in complete graphs. Keywords: genetic algorithm, graphs, population structure, population dynamics. [ABSTRACT FROM AUTHOR]

Ligation is a form of chemical self-assembly that involves dynamic formation of strong covalent bonds in the presence of weak associative forces. We study an extremely simple form of ligation by means of a dissipative particle dynamics (DPD) model extended to include the dynamic making and breaking of strong bonds, which we term dynamically bonding dissipative particle dynamics (DDPD). Then we use a chemical genetic algorithm (CGA) to optimize the model's parameters to achieve a limited form of ligation of trimers—a proof of principle for the evolutionary design of self-assembling chemical systems. [ABSTRACT FROM AUTHOR]