Total Alignment

ALIGN YOUR BUSINESS FOR SUCCESS From overarching vision to individual competency scorecards, Total Alignment arms you with powerful concepts and tools to run a successful, efficient business. No matter what size or type of business you run, business strategy experts Riaz Khadem and Linda Khadem show you how to align your team and operations from the ground up and from the top down. Total Alignment is the result of innovative thinking, solid research, and thirty successful years of consulting experience with major companies. Whether your team struggles most with communication, accountability, or motivation, this book will help you inspire your organization to produce efficiently, engage in the company's vision, and hold each other accountable for solid, sustained progress. Implement these concepts and tools to gain coherence, strength, and value: • Measure and narrow alignment gaps in key areas of your business using the Alignment Survey • Plan for your company’s growth and measure it along the way with the Alignment Map • Define clear roles and responsibilities for each member of your team to ensure accountability with Accountability Assignment worksheets • Eliminate silos, inefficiencies, and redundancies with the one page management strategy • Set short- and long-term goals that add value to each branch of the company as well as the business as a whole Plus, gain access to easy-to-use templates to analyze your company’s alignment, including Business and Individual Scorecards, the Competency Worksheet, an Action Plan Commitment chart, and the Performance and Effort Indexes.

This book is a teachers’ and administrators’ guide for implementing and sustaining an educational system that ensures students are taught and learn what is required by benchmarks, assessments, and state standards, and to the learning needs of each individual student. This is accomplished by providing a tight alignment between the intended, taught, and tested curricula.

Covers the fundamentals and techniques of multiple biological sequence alignment and analysis, and shows readers how to choose the appropriate sequence analysis tools for their tasks This book describes the traditional and modern approaches in biological sequence alignment and homology search. This book contains 11 chapters, with Chapter 1 providing basic information on biological sequences. Next, Chapter 2 contains fundamentals in pair-wise sequence alignment, while Chapters 3 and 4 examine popular existing quantitative models and practical clustering techniques that have been used in multiple sequence alignment. Chapter 5 describes, characterizes and relates many multiple sequence alignment models. Chapter 6 describes how traditionally phylogenetic trees have been constructed, and available sequence knowledge bases can be used to improve the accuracy of reconstructing phylogeny trees. Chapter 7 covers the latest methods developed to improve the run-time efficiency of multiple sequence alignment. Next, Chapter 8 covers several popular existing multiple sequence alignment server and services, and Chapter 9 examines several multiple sequence alignment techniques that have been developed to handle short sequences (reads) produced by the Next Generation Sequencing technique (NSG). Chapter 10 describes a Bioinformatics application using multiple sequence alignment of short reads or whole genomes as input. Lastly, Chapter 11 provides a review of RNA and protein secondary structure prediction using the evolution information inferred from multiple sequence alignments. • Covers the full spectrum of the field, from alignment algorithms to scoring methods, practical techniques, and alignment tools and their evaluations • Describes theories and developments of scoring functions and scoring matrices •Examines phylogeny estimation and large-scale homology search Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications is a reference for researchers, engineers, graduate and post-graduate students in bioinformatics, and system biology and molecular biologists. Ken Nguyen, PhD, is an associate professor at Clayton State University, GA, USA. He received his PhD, MSc and BSc degrees in computer science all from Georgia State University. His research interests are in databases, parallel and distribute computing and bioinformatics. He was a Molecular Basis of Disease fellow at Georgia State and is the recipient of the highest graduate honor at Georgia State, the William M. Suttles Graduate Fellowship. Xuan Guo, PhD, is a postdoctoral associate at Oak Ridge National Lab, USA. He received his PhD degree in computer science from Georgia State University in 2015. His research interests are in bioinformatics, machine leaning, and cloud computing. He is an editorial assistant of International Journal of Bioinformatics Research and Applications. Yi Pan, PhD, is a Regents' Professor of Computer Science and an Interim Associate Dean and Chair of Biology at Georgia State University. He received his BE and ME in computer engineering from Tsinghua University in China and his PhD in computer science from the University of Pittsburgh. Dr. Pan's research interests include parallel and distributed computing, optical networks, wireless networks and bioinformatics. He has published more than 180 journal papers with about 60 papers published in various IEEE/ACM journals. He is co-editor along with Albert Y. Zomaya of the Wiley Series in Bioinformatics.

This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.

Business executives must ensure that their corporate positioning, product positioning, value propositions, sales channel strategies, messaging, and targeting are all in true alignment with each other, as well as the expectations of a target market.

The sequencing of the human genome involved thousands of scientists but used relatively few tools. Today, obtaining sequences is simpler, but aligning the sequences—making sure that sequences from one source are properly compared to those from other sources—remains a complicated but underappreciated aspect of comparative molecular biology. This volume, the first to focus on this crucial step in analyzing sequence data, is about the practice of alignment, the procedures by which alignments are established, and more importantly, how the outcomes of any alignment algorithm should be interpreted. Edited by Michael S. Rosenberg with essays by many of the field's leading experts, Sequence Alignment covers molecular causes, computational advances, approaches for assessing alignment quality, and philosophical underpinnings of the algorithms themselves.

This book develops a new approach called parameter advising for finding a parameter setting for a sequence aligner that yields a quality alignment of a given set of input sequences. In this framework, a parameter advisor is a procedure that automatically chooses a parameter setting for the input, and has two main ingredients: (a) the set of parameter choices considered by the advisor, and (b) an estimator of alignment accuracy used to rank alignments produced by the aligner. On coupling a parameter advisor with an aligner, once the advisor is trained in a learning phase, the user simply inputs sequences to align, and receives an output alignment from the aligner, where the advisor has automatically selected the parameter setting. The chapters first lay out the foundations of parameter advising, and then cover applications and extensions of advising. The content • examines formulations of parameter advising and their computational complexity, • develops methods for learning good accuracy estimators, • presents approximation algorithms for finding good sets of parameter choices, and • assesses software implementations of advising that perform well on real biological data. Also explored are applications of parameter advising to • adaptive local realignment, where advising is performed on local regions of the sequences to automatically adapt to varying mutation rates, and • ensemble alignment, where advising is applied to an ensemble of aligners to effectively yield a new aligner of higher quality than the individual aligners in the ensemble. The book concludes by offering future directions in advising research.