Products related to Processes:
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Technology Innovation : Models, Dynamics, and Processes
Technology Innovation discusses the fundamental aspects of processes and structures of technology innovation.It offers a new perspective concerning fundamentals aspects not directly involved in the complex relations existing between technology and the socio-economic system. By considering technology and its innovation from a scientific point of view, the book presents a novel definition of technology as a set of physical, chemical and biological phenomena, producing an effect exploitable for human purposes.Expanding on the general model of technology innovation by linking the model of technology, based on a structure of technological operations, with the models of the structures for technology innovation, based on organization of fluxes of knowledge and capitals, the book considers various technological processes and the stages of the innovation process. Explains a novel definition of technology as a set of physical, chemical and biological phenomena producing an effect exploitable for human purposesDiscusses technology innovation as result of structures organizing fluxes of knowledge and capitalsProvides a technology model simulating the functioning of technology with its optimizationPresents a technology innovation model explaining the territorial technology innovation processOffers a perspective on the evolution of technology in the frame of an industrial platform networkThe book is intended for academics, graduate students and technology developers who are involved in operations management and research, innovation and technology development.
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Manufacturing Technology : Manufacturing Processes
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Design for Sustainability : Green Materials and Processes
Design for Sustainability: Green Materials and Processes provides fundamental and practical knowledge surrounding product development applications throughout the entire lifecycle of green materials, ranging from conceptual design, material and manufacturing process selection, and environmental lifecycle assessment.In addition, several topics covering recent advances in the application of sustainable design within the automotive, building and construction, packaging and consumer product industries are also included in this book to provide practical examples of this philosophy in current applications.Lastly, a section on implementation of design for sustainability in education is added to aid readers that wish to introduce this philosophy to younger students. This book will be beneficial to researchers, students in higher education institutions, design practitioners and engineers in private and public sector organization with aspirations to develop sustainable products in the future. Design for sustainability is one of the primary focuses in human advancement nowadays, with the aim of developing products and services that meet the needs of the present without compromising the ability of future generations to meet their own needs.
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Making Interdisciplinary Performance : Processes and Practices in Collaboration
This collection offers an examination of and guide to interdisciplinary collaboration through the working practices of performance makers, exploring its pleasures, problems and pitfalls. The book explores contemporary working practices at the interfaces between performance and other disciplines.Focusing on collaborations between theatre makers and these ‘others’, it investigates the processes and conditions involved in the interdisciplinary.Chapters cover areas such as psychology, the environment, physical cultures, the military, healthcare, festivals and communities, architecture, pedagogy and fine dining. At a time when interdisciplinary practice is actively encouraged in the academy, this book asks what it means in practical terms to engage with artists and scholars outside of our home territories.Where does the interdisciplinary exist and what does it rely on?How do theatre makers approach and sustain these projects?What is the nature of the journey? Offering examples of unusual collaborations and arranged through 3 key areas of performance-making, this book acknowledges the difficulties of such work and that the aims of interdisciplinarity can fail, while examining how new methods and understandings can shift notions of knowledge.
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What are growth processes in mathematics?
Growth processes in mathematics refer to the development and expansion of mathematical concepts, skills, and understanding over time. This can involve building on foundational knowledge, mastering new techniques, and making connections between different areas of mathematics. Growth processes also include the ability to solve increasingly complex problems, think critically, and apply mathematical reasoning in various contexts. Ultimately, growth in mathematics is a continuous journey of learning and improvement that leads to a deeper and more comprehensive understanding of the subject.
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How can growth processes be modeled?
Growth processes can be modeled using mathematical equations and statistical methods. One common approach is to use exponential or logistic growth models to describe how a quantity changes over time. These models can be used to predict future growth based on past data and to understand the underlying mechanisms driving the growth process. Additionally, growth processes can also be modeled using computer simulations and agent-based models to capture the complex interactions and feedback loops that influence growth in biological, economic, and social systems.
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How do exponential growth processes occur?
Exponential growth processes occur when a quantity increases at a constant percentage rate over a fixed period of time. This means that the rate of growth itself is increasing, leading to a rapid and accelerating increase in the quantity. Exponential growth can be seen in various natural and man-made systems, such as population growth, compound interest, and the spread of infectious diseases. It is important to note that exponential growth is unsustainable in the long term, as it eventually leads to resource depletion or other limiting factors.
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How do growth processes work in mathematics?
In mathematics, growth processes refer to the way in which quantities increase or decrease over time or with respect to another variable. These processes can be modeled using various mathematical functions such as linear, exponential, or logarithmic functions. Growth processes can be described by equations that show how a quantity changes in relation to another variable, allowing for predictions and analysis of the behavior of the system. Understanding growth processes in mathematics is essential for various applications such as population growth, compound interest, and exponential decay.
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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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Designing for Diversity : Developing Inclusive and Equitable Talent Management Processes
What measures do you use to spot talent and measure employee performance?How do you decide who gets promoted? Are these methods fair and inclusive?Organizations are keen to recruit and retain a diverse workforce but the talent management processes in place are often biased.The measures used and how organizations are identifying and assessing which employees should progress in the business are flawed.Talent Management processes need to be fair, equitable and inclusive and most importantly, they need to be designed with diversity in mind.This book has the answer. Designing for Diversity shows organizations how to rethink their talent management structures, processes and activity so that all employees are supported to develop and progress, not just a select few.It explores each element of talent management from performance management systems and talent identification methods through to talent development and promotion processes.This book also examines the limitations of target setting and the benefits of both formal and informal mentoring.The final part of the book is a full action plan for how to design talent management for diversity to ensure organizations have a diverse workforce performing to their full potential and a rigorous succession plan in place.There are real-world examples throughout from organizations including BT, NFU Mutual, University of Cambridge, HSBC, arm, Neptune Energy, NATS (National Air Traffic Services), Sopra Steria and England Rugby.
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Stationary Processes and Discrete Parameter Markov Processes
This textbook explores two distinct stochastic processes that evolve at random: weakly stationary processes and discrete parameter Markov processes.Building from simple examples, the authors focus on developing context and intuition before formalizing the theory of each topic.This inviting approach illuminates the key ideas and computations in the proofs, forming an ideal basis for further study. After recapping the essentials from Fourier analysis, the book begins with an introduction to the spectral representation of a stationary process.Topics in ergodic theory follow, including Birkhoff's Ergodic Theorem and an introduction to dynamical systems.From here, the Markov property is assumed and the theory of discrete parameter Markov processes is explored on a general state space.Chapters cover a variety of topics, including birth-death chains, hitting probabilities and absorption, the representation of Markov processes as iterates of random maps, and large deviation theory for Markov processes.A chapter on geometric rates of convergence to equilibrium includes a splitting condition that captures the recurrence structure of certain iterated maps in a novel way.A selection of special topics concludes the book, including applications of large deviation theory, the FKG inequalities, coupling methods, and the Kalman filter. Featuring many short chapters and a modular design, this textbook offers an in-depth study of stationary and discrete-time Markov processes.Students and instructors alike will appreciate the accessible, example-driven approach and engaging exercises throughout.A single, graduate-level course in probability is assumed.
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Diffusion Processes, Jump Processes, and Stochastic Differential Equations
Diffusion Processes, Jump Processes, and Stochastic Differential Equations provides a compact exposition of the results explaining interrelations between di?usion stochastic processes, stochastic di?erential equations and the fractional in?nitesimal operators.The draft of this book has been extensively classroom tested by the author at Case Western Reserve University in a course that enrolled seniors and graduate students majoring in mathematics, statistics, engineering, physics, chemistry, economics and mathematical ?nance.The last topic proved to be particularly popular among students looking for careers on Wall Street and in research organizations devoted to ?nancial problems.Features Quickly and concisely builds from basic probability theory to advanced topics Suitable as a primary text for an advanced course in diffusion processes and stochastic differential equations Useful as supplementary reading across a range of topics.
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What are exponential growth and decay processes?
Exponential growth and decay processes are mathematical models that describe how a quantity changes over time. In exponential growth, the quantity increases at a rate proportional to its current value, resulting in rapid growth over time. On the other hand, exponential decay describes a process where the quantity decreases at a rate proportional to its current value, leading to a rapid decline over time. These processes are commonly used to model population growth, radioactive decay, and financial investments.
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Where is the error in mathematical growth processes?
The error in mathematical growth processes typically arises from incorrect assumptions or inputs in the calculations. This could include using inaccurate data, applying the wrong formula, or making a mistake in the calculations. It is important to double-check all inputs and calculations to ensure the accuracy of the growth process and avoid errors.
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What are the mathematical models for growth processes?
Mathematical models for growth processes typically involve exponential or logistic functions. Exponential growth models assume that a population grows at a constant percentage rate over time, while logistic growth models take into account limiting factors that eventually slow down the growth rate. These models are often used in biology, economics, and other fields to predict and understand how populations or quantities change over time. They can help in making projections, understanding trends, and making informed decisions based on the growth patterns.
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How do you calculate growth and decline processes?
Growth and decline processes can be calculated using a simple formula: Growth/Decline Rate = (Final Value - Initial Value) / Initial Value * 100. This formula calculates the percentage change in a value over a specific period of time. For example, if a company's revenue increased from $100,000 to $150,000 over a year, the growth rate would be (150,000 - 100,000) / 100,000 * 100 = 50%. Similarly, if a population decreased from 1,000 to 800 over a decade, the decline rate would be (800 - 1,000) / 1,000 * 100 = -20%.
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