In this international collection of papers there is a wealth of knowledge on artificial intelligence (AI) and cognitive science (CS) techniques applied to the problem of providing help systems mainly for the UNIX operating system. The research described here involves the representation of technical computer concepts, but also the representation of how users conceptualise such concepts. The collection looks at computational models and systems such as UC, Yucca, and OSCON programmed in languages such as Lisp, Prolog, OPS-5, and C which have been developed to provide UNIX help. These systems range from being menu-based to ones with natural language interfaces, some providing active help, intervening when they believe the user to have misconceptions, and some based on empirical studies of what users actually do while using UNIX. Further papers investigate planning and knowledge representation where the focus is on discovering what the user wants to do, and figuring out a way to do it, as well as representing the knowledge needed to do so. There is a significant focus on natural language dialogue where consultation systems can become active, incorporating user modfelling, natural language generation and plan recognition, modelling metaphors, and users' mistaken beliefs. Much can be learned from seeing how AI and CS techniques can be investigated in depth while being applied to a real test-bed domain such as help on UNIX.

Cardiovascular disease affects 20% of all Americans and is the leading cause of death in the USA. Every year approximately 1,500.000 Americans suffer from an acute myocardial infarction, and over 25% of the total number of deaths in this country are attributable to coronary artery disease. Unstable angina causes over 650,000 hospital admissions, and in the absence of medical therapy, the rate of progression from unstable angina to infarction is 12-20%. Since plaque disruption, plaque erosion, and acute intracoronary thrombus formation play major roles in the pathophysiology of both unstable angina and acute myocardial infarction, and since the two disease entities represent a continuum of coronary ischemic syndromes, it is appropriate to discuss the two disease states in a single monograph. Unstable Coronary Artery Syndromes, Pathophysiology, Diagnosis and Treatment will offer the clinician a reference book which coherently discusses the pathophysiology of acute ischemic syndromes as well as remarks diagnosis and treatment strategies. Geared toward internists and cardiologists, the book should allow the reader to understand the pathophysiology of unstable coronary artery syndromes and subsequently apply this knowledge to patient care.

Cardiovascular disease affects 20% of all Americans and is the leading cause of death in the USA. Every year approximately 1,500.000 Americans suffer from an acute myocardial infarction, and over 25% of the total number of deaths in this country are attributable to coronary artery disease. Unstable angina causes over 650,000 hospital admissions, and in the absence of medical therapy, the rate of progression from unstable angina to infarction is 12-20%. Since plaque disruption, plaque erosion, and acute intracoronary thrombus formation play major roles in the pathophysiology of both unstable angina and acute myocardial infarction, and since the two disease entities represent a continuum of coronary ischemic syndromes, it is appropriate to discuss the two disease states in a single monograph. Unstable Coronary Artery Syndromes, Pathophysiology, Diagnosis and Treatment will offer the clinician a reference book which coherently discusses the pathophysiology of acute ischemic syndromes as well as remarks diagnosis and treatment strategies. Geared toward internists and cardiologists, the book should allow the reader to understand the pathophysiology of unstable coronary artery syndromes and subsequently apply this knowledge to patient care.

In this international collection of papers there is a wealth of knowledge on artificial intelligence (AI) and cognitive science (CS) techniques applied to the problem of providing help systems mainly for the UNIX operating system. The research described here involves the representation of technical computer concepts, but also the representation of how users conceptualise such concepts. The collection looks at computational models and systems such as UC, Yucca, and OSCON programmed in languages such as Lisp, Prolog, OPS-5, and C which have been developed to provide UNIX help. These systems range from being menu-based to ones with natural language interfaces, some providing active help, intervening when they believe the user to have misconceptions, and some based on empirical studies of what users actually do while using UNIX. Further papers investigate planning and knowledge representation where the focus is on discovering what the user wants to do, and figuring out a way to do it, as well as representing the knowledge needed to do so. There is a significant focus on natural language dialogue where consultation systems can become active, metaphors, and users' mistaken beliefs. Much can be learned from seeing how AI and CS techniques can be investigated in depth while being applied to a real test-bed domain such as help on UNIX.