Products related to Complexity:
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Navigating the Complexity Across the Peace–Sustainability–Climate Security Nexus
Promoting peace and sustainability in human development while accounting for the risks associated with the impact of climate change on society has become more imperative than ever when addressing humanity's challenges of the twenty-first century.There is enough evidence that peace, sustainability, and climate security are entangled with multiple complex interactions and cannot be dealt with in isolation and independently from the environment and the numerous systems with which they interact.Yet, the intersection of peace, sustainability, and climate security or their opposites (i.e., conflict, unsustainability, and climate vulnerability) is rarely articulated with a systemic mindset.A multi-solving nexus approach is more appropriate to capture the complexity and uncertainty of how the three sectors of peace, sustainability, and climate security play a role in community development, the nature of their causal chains, and the feedback on how community development affects the three sectors.Navigating the Complexity Across the Peace–Sustainability–Climate Security Nexus explores the value proposition of using a systems approach, methodology, and tools to comprehend and model that dynamic. Features of the book: Explores the interaction between the different components of peace and the relationship between peace, sustainability, and climate security using semi-qualitative and quantitative tools; Explains how climate adaptation and mitigation are related to peace or conflict; Presents generic system dynamics modeling that can be used in different contexts.
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The Glycome : Understanding the Diversity and Complexity of Glycobiology
This volume provides a comprehensive understanding of the enigmatic identity of the glycome, a complex but important area of research that has been largely ignored due to its complexity.The authors thoroughly deal with almost all aspects of the glycome, i.e., elucidation of the glycan identity enigma and its role in regulation of the cellular process, and in disease etiology.The book bridges the knowledge gap in understanding the glycome, from being a cell signature to its applications in disease etiology.In addition, it details many of the major insights regarding the possible role of the glycome in various diseases as a therapeutic marker. The book systematically covers the major aspects of the glycome, including the significance of substituting the diverse monosaccharide units to glycoproteins, the role of glycans in disease pathologies, and the challenges and advances in glycobiology.The authors stress the significance and huge encoding power of carbohydrates as well as provide helpful insights in framing the bigger picture. The Glycome: Understanding the Diversity and Complexity of Glycobiology details state-of-the-art developments and emerging challenges of glycome biology, which are going to be key areas of future research, not only in the glycobiology field but also in pharmaceutics.
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Normal Family Processes, Fourth Edition : Growing Diversity and Complexity
Widely adopted, this valued course text and practitioner guide has expanded the understanding of family normality and healthy functioning in our increasingly diverse society.The editor and contributors are at the forefront of research and clinical training.They describe the challenges facing contemporary families and ways in which clinicians can promote resilience.With consideration of sociocultural and developmental influences, chapters identify key family processes that nurture and sustain strong bonds in couples; dual-earner, divorced, single-parent, remarried, adoptive, and kinship care families; gay and lesbian families; culturally diverse families; and those coping with adversity, such as trauma, poverty, and chronic illness.New to This Edition *Reflects important research advances and the changing contexts of family life. *Additional chapter topics: kinship care, family rituals, evidence-based assessment, and neurobiology. *All chapters have been fully updated.
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Recentering Learning : Complexity, Resilience, and Adaptability in Higher Education
Is a renaissance of teaching and learning in higher education possible?One may already be underway. The COVID-19 pandemic fundamentally changed how colleges and universities manage teaching and learning.Recentering Learning unpacks the wide-reaching implications of disruptions such as the pandemic on higher education. Editors Maggie Debelius, Joshua Kim, and Edward Maloney assembled a diverse group of scholars and practitioners to assess the impacts of the pandemic, as well as to anticipate the effects of climate change, social unrest, artificial intelligence, financial challenges, changing demographics, and other forms of disruption, on teaching and learning.These contributors are leaders at their institutions and draw on both the Scholarship of Teaching and Learning (SoTL) as well as their lived experiences to draw important lessons for the wider postsecondary ecosystem.The collection features faculty, staff, and student voices from a range of public and private institutions of varying sizes and serving different populations. Covering timely topics such as institutional resiliency, how to create transformational change, digital education for access and equity, and the shifting institutional data landscape, these essays serve as a compelling guide for how colleges and universities can navigate inevitable changes to teaching and learning.Faculty and staff at centers for teaching excellence or centers for innovation, university leaders, graduate students in learning design programs, and anyone interested in the evolution of teaching and learning in the twenty-first century will benefit from this prescient volume. Contributors: Bryan Alexander, Drew Allen, Isis Artze-Vega, Betsy Barre, Randy Bass, MJ Bishop, Derek Bruff, Molly Chehak, Nancy Chick, Cynthia A.Cogswell, Jenae Cohn, Tazin Daniels, Maggie Debelius, David Ebenbach, Megan Eberhardt-Alstot, Kristen Eshleman, Peter Felten, Lorna Gonzalez, Michael Goudzwaard, Sophia Grabiec, Sean Hobson, Kashema Hutchinson, Amanda Irvin, Jonathan Iuzzini, Amy Johnson, Briana Johnson, Matthew Kaplan, Whitney Kilgore, Joshua Kim, Sujung Kim, Suzanna Klaf, Martin Kurzweil, Natalie Landman, Jill Leafstedt, Katie Linder, Sherry Linkon, Edward Maloney, Susannah McGowan, Isabel McHenry, Rolin Moe, Lillian Nagengast, Nancy O'Neill, Adashima Oyo, Matthew Rascoff, Libbie Rifkin, Katina Rogers, Catherine Ross, Annie Sadler, Monique L.Snowden, Elliott Visconsi, Mary Wright
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What is the complexity of semiconductor technology or microsystems technology?
The complexity of semiconductor technology or microsystems technology is high due to the intricate processes involved in designing, manufacturing, and integrating tiny electronic components. These technologies require precise control at the nanoscale level, involving complex materials, intricate fabrication techniques, and sophisticated equipment. Additionally, the rapid pace of innovation and the need for continuous improvement in performance and miniaturization add to the complexity of these technologies. As a result, semiconductor and microsystems technology require significant expertise, resources, and investment to develop and produce advanced electronic devices.
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Can complexity be objectively measured?
Complexity can be objectively measured to some extent, especially in the context of information theory and algorithmic complexity. In information theory, complexity can be measured using metrics such as entropy and Kolmogorov complexity, which provide objective measures of the amount of information or computational resources required to describe a system. However, when it comes to measuring the complexity of real-world systems or phenomena, there is often a subjective element involved, as different observers may prioritize different aspects of complexity. Therefore, while certain aspects of complexity can be objectively measured, the overall assessment of complexity may still involve some degree of subjectivity.
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What is the complexity of Mergesort?
The time complexity of Mergesort is O(n log n) in the worst-case scenario, where n is the number of elements in the array. This complexity arises from the fact that Mergesort divides the array into halves recursively and then merges them back together in sorted order. The space complexity of Mergesort is O(n) due to the need for additional space to store the divided subarrays during the sorting process. Overall, Mergesort is an efficient sorting algorithm that performs well on large datasets.
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How can one get rid of complexity?
One can get rid of complexity by breaking down the problem or situation into smaller, more manageable parts. This can help to identify the root causes of the complexity and address them individually. Additionally, simplifying processes, communication, and decision-making can help reduce complexity. It is also important to prioritize and focus on the most important aspects, while letting go of unnecessary details. Finally, seeking input and collaboration from others can provide fresh perspectives and help to streamline complex situations.
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Think Complexity : Complexity Science and Computational Modeling
Complexity science uses computation to explore the physical and social sciences.In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
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Art and Sustainability : Connecting Patterns for a Culture of Complexity
What is the cultural dimension of sustainability? This book offers a thought-provoking answer, with a theoretical synthesis on »cultures of sustainability«.Describing how modernity degenerated into a culture of unsustainability, to which the arts are contributing, Sacha Kagan engages us in a fundamental rethinking of our ways of knowing and seeing the world.We must learn not to be afraid of complexity, and to re-awaken a sensibility to patterns that connect.With an overview of ecological art over the past 40 years, and a discussion of art and social change, the book assesses the potential role of art in a much needed transformation process.
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Developmental Evaluation : Applying Complexity Concepts to Enhance Innovation and Use
Developmental evaluation (DE) offers a powerful approach to monitoring and supporting social innovations by working in partnership with program decision makers.In this book, eminent authority Michael Quinn Patton shows how to conduct evaluations within a DE framework.Patton draws on insights about complex dynamic systems, uncertainty, nonlinearity, and emergence.He illustrates how DE can be used for a range of purposes: ongoing program development, adapting effective principles of practice to local contexts, generating innovations and taking them to scale, and facilitating rapid response in crisis situations.Students and practicing evaluators will appreciate the book's extensive case examples and stories, cartoons, clear writing style, "closer look" sidebars, and summary tables.Provided is essential guidance for making evaluations useful, practical, and credible in support of social change. See also Developmental Evaluation Exemplars, edited by Michael Quinn Patton, Kate McKegg, and Nan Wehipeihana, which presents 12 in-depth case studies.
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Simplified Complexity
Thanks to the growth of computational power and the development of new productiontechnologies, NURBS modeling has become the standard in many fields:Industrial Design, Architecture and, more recently, Engineering.Simplified Complexity is a method for learning NURBS modeling with Rhinoceros (R).Born as the synthesis of twenty years of professional experience and teaching,Simplified Complexity consists of a structured knowledge system allowing deepunderstanding of the software.With this method the user can take advantage of Rhinoceros (R) full modeling potential.The idea behind Simplified Complexity is that even if the software has a clear andintuitive interface, NURBS geometry remains quite complex.In order to become aprofessional user, it is necessary to start from basic geometry knowledge: this willallow to foresee and avoid complexity or, if this is not possible, at least reduce it andoptimize it.
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What is the complexity of composing two functions?
Composing two functions has a complexity of O(1), as it involves simply applying one function to the output of the other. The time complexity does not depend on the size of the input, as the functions are applied sequentially. Therefore, the complexity of composing two functions is constant and does not increase with the size of the input.
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What are the Landau symbols for the time complexity?
The Landau symbols for time complexity are commonly used to describe the upper and lower bounds of an algorithm's running time. The most commonly used Landau symbols for time complexity are O (big O) for upper bound, Ω (big omega) for lower bound, and Θ (big theta) for both upper and lower bounds. These symbols are used to express the growth rate of an algorithm's running time in terms of the input size. For example, if an algorithm has a time complexity of O(n^2), it means that the running time of the algorithm grows no faster than n^2 as the input size increases.
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What are the Big O notations for time complexity?
The Big O notations for time complexity are used to describe the upper bound on the growth rate of an algorithm's running time as the input size increases. Some common Big O notations include O(1) for constant time complexity, O(log n) for logarithmic time complexity, O(n) for linear time complexity, O(n^2) for quadratic time complexity, and O(2^n) for exponential time complexity. These notations help in analyzing and comparing the efficiency of different algorithms.
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How do you determine the complexity of a function?
The complexity of a function can be determined by analyzing its time and space requirements. This can be done by examining the number of operations the function performs and the amount of memory it uses. Additionally, the complexity can be influenced by the size of the input data and the efficiency of the algorithm used in the function. By considering these factors, one can determine the complexity of a function, which is often expressed using Big O notation.
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