Showing posts with label risk. Show all posts
Showing posts with label risk. Show all posts

Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research & Management Science) Review

Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research and Management Science)
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This is an excellent, approachable read for any risk manager; understanding its examples requires only elementary probability, statistics and calculus, though the foundations are much deeper. The author uses direct language, and does not hesitate to declare a fashionable risk analysis technique "worse than useless." The author shows how not to do risk analysis, using simple but devastating examples to illustrate the weaknesses of prioritized investments, subject matter expert opinion, risk matrices and qualitative risk assessments, and the independence assumption. Then, case studies present constructive examples of good practice. Refreshingly, this text clearly distinguishes between threats from Mother Nature, and those posed by an intelligent adversary. There is unevenness, because this is an edited ensemble of papers originally published in a variety of technical journals; however, this is also a strength, because the appeal and scholarship underlying biological, engineering, and social science examples is broad. This is not a how-to guide, and won't help fill in a blank page risk analysis; however, this is an excellent source for the skeptical consumer of contemporary risk management advice and products, and hopefully will have some influence with policy makers who are the source of simplistic and dangerous guidance.

Gerald G. Brown
Distinguished Professor of Operations Research
Naval Postgraduate School
National Academy of Engineering
INFORMS Fellow


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In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems - systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence - and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

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The Failure of Risk Management: Why It's Broken and How to Fix It Review

The Failure of Risk Management: Why It's Broken and How to Fix It
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How do we know if our risk management methods are working? Would we notice if they were not working? What are the consequences if they are not working? These are the three basic questions that Douglas Hubbard asks in his book The Failure of Risk Management.
In this book Mr. Hubbard lays out the basics of risk management and explains why many risk management methods are worse than useless. He also provides some ideas and first steps to fix the problem.
Here's a brief walk though 'The Failure of Risk Management':
Part I introduces the history of risk management and the problems with modern risk management methods. Independent events, for instance, are often times not independent at all. This common-mode failure is unaccounted for by many managers, yet can be devastating in an emergency.
Part II of the book goes in depth with some of the problems and failures of risk management, and to me was the most interesting part of the book. Chapter 4 is called The "Four Horseman" of Risk Management, and describes the differences between what the author considers the four main classes of risk managers. The four classes are actuaries, "war quants," economists, and management consultants. Each group has distinctly different methods and areas of expertise, as well as different levels of validation.
Chapter 5 is about how risk should be defined, and why different people may actually be talking about different things when they discuss volatility and risk. Chapter 6 breaks down why humans are not good at subjective methods (which lays the ground work for later chapters introducing quantitative methods). There are a few "calibration" tests available for you to see how overconfident you are in your decision making. These are pretty interesting, and even after reading about overconfidence I still did poorly on them.
Chapters 7, 8, and 9 talk about problems with subjective scoring methods, problems with describing one-off events, and the problems with some quantitative models. The author talks about "black swans," as described by Nassim Nicholas Taleb, and how they relate to modeling. Many times people believe that events can't be modeled, but the author believes this is not so.
The last section of the book, Part III, gives some ideas on how to fix risk management. Adding empiricism is a big start, as well as calibration of subjective human inputs. Many companies build and use models, but then they don't actually bother to see how well the things have performed in the past. I will leave the rest of the solutions for you to read in the book.
Recommendation:
First off, the author says this book is geared towards all types of risk management, and all types of industries, and I think this is true. The author uses a wide variety of examples from airplane engine failures to volcano eruptions. But I still feel like this book is more geared towards enterprise risk management, and less towards the already quant heavy fields such as actuarial science or credit risk management. But it was an interesting read nonetheless.
It seems like in the past 20 years there have been several so-called "once-in-a-lifetime events," such as the floods of Hurricane Katrina or any of the financial crisis, including 1987, 1998, 2000, or 2008. I wish I had the money to buy this book for every person who ever said "no one saw that coming."
I think this is a great book for anyone who deals with the potential for risk, loss, or damage - no matter if it is financial, personal, or physical. When the stakes are high we should be careful relying on a risk matrix developed by a management consultant, and Douglas Hubbard will tell you why. If you work in risk management, or if you have influence on the operational strategy of some organization, then this book is a must read.


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An essential guide to the calibrated risk analysis approach
The Failure of Risk Management takes a close look at misused and misapplied basic analysis methods and shows how some of the most popular "risk management" methods are no better than astrology! Using examples from the 2008 credit crisis, natural disasters, outsourcing to China, engineering disasters, and more, Hubbard reveals critical flaws in risk management methods–and shows how all of these problems can be fixed. The solutions involve combinations of scientifically proven and frequently used methods from nuclear power, exploratory oil, and other areas of business and government. Finally, Hubbard explains how new forms of collaboration across all industries and government can improve risk management in every field.
Douglas W. Hubbard (Glen Ellyn, IL) is the inventor of Applied Information Economics (AIE) and the author of Wiley's How to Measure Anything: Finding the Value of Intangibles in Business (978-0-470-11012-6), the #1 bestseller in business math on Amazon. He has applied innovative risk assessment and risk management methods in government and corporations since 1994.
"Doug Hubbard, a recognized expert among experts in the field of risk management, covers the entire spectrum of risk management in this invaluable guide. There are specific value-added take aways in each chapter that are sure to enrich all readers including IT, business management, students, and academics alike"—Peter Julian, former chief-information officer of the New York Metro Transit Authority. President of Alliance Group consulting
"In his trademark style, Doug asks the tough questions on risk management. A must-read not only for analysts, but also for the executive who is making critical business decisions."—Jim Franklin, VP Enterprise Performance Management and General Manager, Crystal Ball Global Business Unit, Oracle Corporation.

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Active Credit Portfolio Management in Practice (Wiley Finance) Review

Active Credit Portfolio Management in Practice (Wiley Finance)
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Bohn and Stein are two of the great innovators in quantitative credit risk, who translated theory into commercial implementation and introduced many of the measurement and validation methods currently used in practice today. This book draws from the wealth of their experience and knowledge and is a masterful overview of single obligor default probability models (structural, econometric, reduced form), model validation, and portfolio models.
Importantly, Bohn and Stein's book possesses something many simply technical works on quantitative credit modeling lack: a point of view. Throughout the book, the authors offer opinions, insights, critiques, and even humor on credit modeling issues. The authors' frankness about just how much judgment goes into the credit risk modeling process is refreshing. Bohn and Stein also offer advice on issues that are not treated in typical credit risk books, such as how to effectively manage a credit research team and practical risk management in a bank.
For those looking for extensive mathematical treatments/proofs of credit risk models, look elsewhere. This is not to say that the book is light on the mathematical details, however. The book contains only the mathematics necessary (which is not inconsequential) for the technical exposition, no more. The book is very readable, and I view its economy as one of the book's strengths rather than a drawback.
This is a book to which I often refer and I cannot recommend it highly enough. A quant's library is incomplete without it.

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Risk Assessment in Geotechnical Engineering Review

Risk Assessment in Geotechnical Engineering
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Two years ago when I started my masters in structural engineering, I had to learn about random field theory. The books that I found, considered the "must reads" for random field theory, might as well have been in Chinese (I don't understand Chinese - just for the record). It was extremely painful and frustrating trying to understand the completely not clear or easy to understand concepts - as they were presented in those books. Upon searching the internet, I found some papers written by Gordon Fenton. His description of random field theory and his work in general, was like an oasis in the desert. This is one very rare person who can explain complicated concepts so that they are easy to understand and apply. So when I found out he was publishing a book, I thought, this is a must have. And I haven't been disappointed. The first section on probability theory is without a doubt, the best explanation of the theory I have seen, with simple examples to help me concrete my understanding of the theory being presented. Therefore, this is the first textbook that I would recommend for people trying to understand the concepts of probability theory. Similarly, all other sections in the book are equally easy to understand and can make even me look intelligent - as he enables me to really understand the concepts being presented, because of his fantastic ability to communicate ' I like in particular, how he explains the practical reasons for why the theory can be simplified or assumptions can be made. Most boffin writers of textbooks don't explain "the obvious" which is not so obvious to the novice person reading their books. Sometimes I think that they don't realise that not everybody has done the mathematics degree required to understand some of the intricacies, which if not clearly explained, can stump the reader for days until they find out why they have assumed this or that. That's where Gordon Fenton is a real a gem. He doesn't leave you scratching your head, scrambling for other textbooks to fill in the gaps before you can continue with his explanations. So I highly recommend this book to anybody trying to understand probability theory, random field theory, estimation, reliability, Monte Carlo simulation and all other topics he covers in his book.

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Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement Review

Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement
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Measuring information security is the greatest remaining challenge for many of us. Metrics are essential for a scientific management approach, rather than relying purely on gut feel and guesswork. Standards such as ISO/IEC 27001 require the use of objective information about the status and effectiveness of information security controls in relation to the risks, in order to drive appropriate improvements in the Information Security Management System. However, it is not immediately obvious exactly what needs measuring, nor how to do it. This book lays out the foundations on which a rational measurement system can be designed to manage information security in a more objective fashion.
The author encourages readers to consider a wide variety of measurement approaches and apply them sensibly to their information security management issues. In addition to conventional information security metrics, the book draws on governance, risk management, financial management and business analysis methods, a more diverse range of approaches than is normally covered in this field. Introducing measures of organization structure and culture sets this security metrics book apart from most others.
Although the writing style is clear, this is a complex subject covered in depth. Being rather theoretical in approach, the book won't suit practitioners simply looking for a short checklist of `security things to measure'. However, those with the interest and time to study Information Security Management Metrics will be rewarded with a deeper and more rounded understanding of the issue. As such, the book is probably of most value to CISOs and ISMs tasked with implementing better security metrics, and to information security management students.

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Spectacular security failures continue to dominate the headlines despite huge increases in security budgets and ever-more draconian regulations. The 20/20 hindsight of audits is no longer an effective solution to security weaknesses, and the necessity for real-time strategic metrics has never been more critical. Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement offers a radical new approach for developing and implementing security metrics essential for supporting business activities and managing information risk. This work provides anyone with security and risk management responsibilities insight into these critical security questions: How secure is my organization?How much security is enough?What are the most cost-effective security solutions?How secure is my organization?Solid metrics are the key to cost-effective information security - you can't manage what you can't measure This volume shows readers how to develop metrics that can be used across an organization to assure its information systems are functioning, secure, and supportive of the organization's business objectives. It provides a comprehensive overview of security metrics, discusses the current state of metrics in use today, and looks at promising new developments. Later chapters explore ways to develop effective strategic and management metrics for information security governance, risk management, program implementation and management, and incident management and response. The book ensures that every facet of security required by an organization is linked to business objectives, and provides metrics to measure it. Case studies effectively demonstrate specific ways that metrics can be implemented across an enterprise to maximize business benefit. With three decades of enterprise information security experience, author Krag Brotby presents a workable approach to developing and managing cost-effective enterprise information security.

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Calculated Bets: Computers, Gambling, and Mathematical Modeling to Win (Outlooks) Review

Calculated Bets: Computers, Gambling, and Mathematical Modeling to Win (Outlooks)
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To knowledge seekers, the ability to understand and beat a system is the entire game. In this book, Skiena describes how he and some of his students wrote a computer program to win money betting on professional jai alai matches. Along the way, he explains the origins of the game and some of the basic rules, the fundamental bets that can be made as well as the meaning of statements such as pari-mutuel betting. His program does work well, in that he quadruples his money in a short time. Once that is done, he gives the money to a university charity, hoping to make his money from writing this book.
The fact that such a program could be created is not surprising. Jai-alai is a sport where individuals compete one-on-one or in teams of two, and the betting patterns determine the payoffs. It is much easier to simulate these types of matchups and predict the outcome than it is for team games. Baseball managers have been doing such modeling for years. If my memory serves me correctly, the first to do it in major league baseball was Davey Johnson, who kept detailed statistics on all pitcher-batter matchups. All of his decisions concerning who to put up to bat were then based on playing the percentages. That is essentially what Skiena does, although with a different twist. Pari-mutuel betting is where those who wager are betting against each other, so the patterns of wagering determine the payoffs. The patterns of betting are also factored into his predictions. These conditions make it possible for someone to make money creating such a system, but only as long as no one else is doing it. If others begin to use the same system, then the players are betting against each other, destroying the opportunity to make a profit. Therefore, his very act of publishing this book probably means that his system can no longer be used to win at jai-alai betting.
This is an excellent example of how basic mathematical modeling is done. Use data of previous results to form a model of what has happened in order to predict what will happen. Skiena writes with a wit and rigor that is rarely seen in mathematics. Very little mathematics background is needed in order to understand the explanations of the behavior of the program and why it works.
I found this book so interesting that I stayed up very late finishing it. It reads like a novel, but teaches you a lot about mathematics. Instructors in mathematical modeling and computer programming can find many interesting ideas for classroom exercises in it. As long as no one takes it too seriously, it is all in good, clean fun.

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Regression Modeling with Actuarial and Financial Applications (International Series on Actuarial Science) Review

Regression Modeling with Actuarial and Financial Applications (International Series on Actuarial Science)
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This text covers regression techniques which the author views as the most commonly used statistical technique especially in the world of insurance and finance. Since the book is in a series for actuarial science I expected the presentation to be elementary to intermediate and to only cover regression. But some of the latter topics were quite advanced. In my mind survival analysis which is extremely important to actuaries and time series analysis which in very important to finance would not be covered as they do not fall into the category of regression. But thankfully they are included.
Some time series models such as polynomial functions of time can be viewed as linear regression models where time is the predictor variable for the response. But the main models like exponetial smoothing, Box-Jenkins and ARCH/GARCH which are the main ones applied in financial forecasting are really not regression in my view. But these too are covered in the book.
The book starts out in Chapter 1 with a very elementary review of statistics and simple forms of regression. Then Chapter 2-6 form part I which is titled Regression. Chapter 2 presents the basics of simple linear regression. Chapters 3 and 4 cover multiple regression. This is not a cookbook of techniques. The author provides background, historical developments and important concepts and mixes in applications to actuarial science and finance throughout. At the end of most chapters are a large number of exercises with solutions for selected problems in the back. In chapter 3 the author explains least squares presents the modeling assumptions and introduces the Gauss-Markovas well as all the standard concepts of hypothesis testing that a regression parameter is significant, R-square and theorem (hence also the concept of minimum variance among unbiased estimators). In Chapter 4 he provides the unified theme of the general linear hypothesis as he covers categorical predictor variables, the analysis of variance and covariance (all general linear models) In Chapter 5, leverage points, multicollinearity, and regression diagnostics are presented in the context of variable selection. Chapter 6 is all about interpretation and limitations.
Later in Parts III and IV the author introduces nonlinear regression models, logistic regression, probit and tobit models, Poisson and negative binomial regression, generalized linear models,and specialized techniques such as bootstrapping, mixed linear models, proportional hazards regression, generalized additive models and the Bayesian approach to regression. The coverage gets more advanced as you move through the chapters
Part II on time series includes seasonal models, discussion of stationary and longitudinal and panel data models.
In Part III survival analysis is included in Chapter 14. This includes the Kaplan-Meier estimates, proportional hazards regression, accelerated failure time models and even the analysis of recurrent events.
Part IV specifically focuses on actuarial applications and it is here that heavytailed distributions are dealt with using quantile regression and extreme value probability models.
With such an extensive list of topics the book is a large volume of over 560 pages. But even so it is not possible to do justice to this extensive list. The author provides an outstanding list of references at the end of the chapters that provides additional reading on the various topics.
In addition the author prvides programs in SAS and R as well as output form these packages. More detailed examples and projects can be found on the books website.

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Statistical techniques can be used to address new situations. This is important in a rapidly evolving risk management and financial world. Analysts with a strong statistical background understand that a large data set can represent a treasure trove of information to be mined and can yield a strong competitive advantage. This book provides budding actuaries and financial analysts with a foundation in multiple regression and time series. Readers will learn about these statistical techniques using data on the demand for insurance, lottery sales, foreign exchange rates, and other applications. Although no specific knowledge of risk management or finance is presumed, the approach introduces applications in which statistical techniques can be used to analyze real data of interest. In addition to the fundamentals, this book describes several advanced statistical topics that are particularly relevant to actuarial and financial practice, including the analysis of longitudinal, two-part (frequency/severity), and fat-tailed data. Datasets with detailed descriptions, sample statistical software scripts in "R" and "SAS," and tips on writing a statistical report, including sample projects, can be found on the book's Web site: http://research.bus.wisc.edu/RegActuaries.

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Applied Dimensional Analysis and Modeling, Second Edition Review

Applied Dimensional Analysis and Modeling, Second Edition
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Thomas Szirtes' _Applied Dimensional Analysis and Modeling_ is an encyclopedic beast of a book. There are many, many worked examples, both standards that are found in Bridgeman and others (like the period of a pendulum), and novel ones.It also has all of the tricks that allow for effective use of the techniques of dimensional analysis in more complicated problems like breaking the mass into inertial and gravitational aspects to solve problems (and send your thoughts down the road of bad philosophy). For this reason alone it is a useful book.
It has some problems, however, that make it difficult for me to recommend the book to someone who doesn't have a good grasp of units, dimensions, and the difference between the two, even though Szirtes intends the book for those with only "an inquisitive mind and a knowledge of basic mechanics and electricity" and "elementary matrix arithmetic." These problems are:
1.Szirtes' use of dimensions and units is non-standard, calling meters a dimension rather than a unit, making ideas like coversion more difficult and some of the examples more convoluted than they need to be,
2.Many of the problems and examples have implied units, so that he might write a formula for velocity in terms of time as v = 9.81 t + 3.2 [this is not in the text, I use it because it's simple],
3.The more mathematical sections include sloppy proofs that, in my view, don't yield any additional understanding.
These are all serious problems for a beginner, who could pick up some bad habits from the book. Something that makes the book a little less useful than it could be is a paucity of electricity and magnetism examples, which are mechanics heavy.
I think this book would be a good introduction for someone with a solid background in physics or engineering or someone who has looked at a less challenging or thorough book in dimensional analysis (such as Bridgeman). I also think it is also a good book for instructors, being a treasure trove of examples, even if they should be sanitized before being given to students.


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Investment Guarantees: The New Science of Modeling and Risk Management for Equity-Linked Life Insurance Review

Investment Guarantees: The New Science of Modeling and Risk Management for Equity-Linked Life Insurance
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As guarantee products are popping up all over the global banking and insurance markets, it is absolutely essential to ensure that the proper financial values are upheld in order to avoid many of the problems that the North American market has faced. 'Investment Guarantees' does a wonderful job of describing these risks in simple enough terms that the pages can be quoted to both financial and non financial people. A very powerful read for those looking to undersand the value of guarantees that are placed on accumulation type insurance products.

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A comprehensive guide to investment guarantees in equity-linked life insuranceDue to the convergence of financial and insurance markets, new forms of investment guarantees are emerging which require financial service professionals to become savvier in modeling and risk management. With chapters that discuss stock return models, dynamic hedging, risk measures, Markov Chain Monte Carlo estimation, and much more, this one-stop reference contains the valuable insights and proven techniques that will allow readers to better understand the theory and practice of investment guarantees and equity-linked insurance policies.Mary Hardy, PhD (Waterloo, Ontario, Canada), is an Associate Professor and Associate Chair of Actuarial Science at the University of Waterloo and is a Fellow of the Institute of Actuaries and an Associate of the Society of Actuaries, where she is a frequent speaker. Her research covers topics in life insurance solvency and risk management, with particular emphasis on equity-linked insurance. Hardy is an Associate Editor of the North American Actuarial Journal and the ASTIN Bulletin and is a Deputy Editor of the British Actuarial Journal.

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Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains Review

Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains
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Some sections are very good others are really bad. Each chapter has a different author who was selected as an expert for that topic. It feels like the overall book was rushed to print without really scrubbing each chapter and tying the whole book together.
Some sections were mainly plugs for the authors research project. Some other section are very focused on using a particular tool. And really... How does a discussion on GUIs fit in a chapter on human behavior modeling?
This book could be so much better but the editors need to put their red pens to work.

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An insightful presentation of the key concepts, paradigms, and applications of modeling and simulation
Modeling and simulation has become an integral part of research and development across many fields of study, having evolved from a tool to a discipline in less than two decades. Modeling and Simulation Fundamentals offers a comprehensive and authoritative treatment of the topic and includes definitions, paradigms, and applications to equip readers with the skills needed to work successfully as developers and users of modeling and simulation.
Featuring contributions written by leading experts in the field, the book's fluid presentation builds from topic to topic and provides the foundation and theoretical underpinnings of modeling and simulation. First, an introduction to the topic is presented, including related terminology, examples of model development, and various domains of modeling and simulation. Subsequent chapters develop the necessary mathematical background needed to understand modeling and simulation topics, model types, and the importance of visualization. In addition, Monte Carlo simulation, continuous simulation, and discrete event simulation are thoroughly discussed, all of which are significant to a complete understanding of modeling and simulation. The book also features chapters that outline sophisticated methodologies, verification and validation, and the importance of interoperability. A related FTP site features color representations of the book's numerous figures.
Modeling and Simulation Fundamentals encompasses a comprehensive study of the discipline and is an excellent book for modeling and simulation courses at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of computational statistics, engineering, and computer science who use statistical modeling techniques.

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