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Abdominal pain is the most common complaint seen in emergency departments in the United States and one of the 10 most common complaints in family medicine outpatient settings. The most common causes of abdominal pain are discussed here, with special attention given to the acute abdomen and recurrent abdominal pain. The term acute abdomen is medical jargon that refers to any acute condition within the abdomen that requires immediate medical or surgical attention. Acute abdominal pain may be of nonabdominal origin and does not always require surgery. The majority of patients who consult a physician about abdominal pain do not have an acute abdomen, although the chief complaint may have a sudden onset. In studies involving analysis of large series of patients presenting to emergency departments with acute abdominal pain, nonspecific abdominal pain (NSAP) was the most common diagnosis. Most patients with this symptom probably have gastroenteritis. The common causes of abdominal pain are gastroenteritis, gastritis, peptic ulcer disease, gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), dysmenorrhea, salpingitis, appendicitis, cholecystitis, cholelithiasis, intestinal obstruction, mesenteric adenitis, diverticulitis, pancreatitis, ureterolithiasis, incarcerated hernias, gas entrapment syndromes, and ischemic bowel disease (particularly in the elderly). All of these conditions can manifest as an acute or sudden onset of abdominal pain, many can cause recurrent abdominal pain, and a few require surgical intervention. Any acute abdominal condition requires the physician to make an early, precise diagnosis, because prognosis often depends on prompt initiation of therapy, particularly surgical treatment. The more serious the problem, the more urgent the need for an accurate diagnosis.
Packaging and dilemmas in packaging development By Professor Roland ten Klooster CurTec International offers packaging and packaging knowhow for industrial and distribution applications in the pharmaceutical, speciality chemicals and other industries. We strongly believe in offering added value through quality, functionality, user-friendliness and design. Added value can be found in many other aspects than just the tangible product. To select or develop the optimal packaging it is essential to have a complete insight in all these aspects. Through the publishing of a series of White Papers on Packaging we hope to make a small contribution to the packaging issues you are confronted with. The CurTec Team White paper | Packaging and dilemmas in packaging development Over 3.5 billion are lost to the cost of packaging, packaging should add value and not be seen as waste’, says Professor ten Klooster. Better packaging can lead to a better product, he claims, which is why he suggests revising and professionalizing the development processes.
Consumer product packaging designers are faced with conflicting requirements throughout the development process. Good pack aesthetics are vital for the success of the product, whilst unit costs must be minimized and suitability for stacking and transportation maintained. This paper describes, by example, how design optimization technology can be used to enhance the design process. It is demonstrated that the technology can be employed to provide clear design information for the pack designers, facilitating definition of an attractive shape incorporating features to meet the structural and manufacturing requirements whilst minimizing cost. Consumer product packaging designers are faced with conflicting requirements throughout the development process. Good pack aesthetics are vital for the success of the product, whilst unit costs must be minimized and suitability for stacking and transportation maintained. A significant improvement in the design process can be gained if design information can be clearly communicated to the product designers early in the design process. This paper describes how design optimization and advanced CAE can be used to deliver this. The resulting design process facilitates the early definition of an attractive pack shape incorporating features which will meet the structural and cost requirements. The design optimization process requires input in the form of a series of alternative shapes for the pack,...
In this paper, we examine a number of SQL and socalled “NoSQL” data stores designed to scale simple OLTP-style application loads over many servers. Originally motivated by Web 2.0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses. We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g. database-wide transaction consistency, in order to achieve others, e.g. higher availability and scalability. Note: Bibliographic references for systems are not listed, but URLs for more information can be found in the System References table at the end of this paper. Caveat: Statements in this paper are based on sources and documentation that may not be reliable, and the systems described are “moving targets,” so some statements may be incorrect. Verify through other sources before depending on information here. Nevertheless, we hope this comprehensive survey is useful! Check for future corrections on the author’s web site cattell.net/datastores. Disclosure: The author is on the technical advisory board of Schooner Technologies and has a consulting business advising on scalable databases.
It has now been a good couple of years since the various anti-SQL proponents have gained enough momentum to come together under the wide umbrella of the term NoSQL. And it is clear that we can never go back: the typical relational database architecture is clearly insufficient for today’s dataintensive applications, and the move to distributed architectures. But is the problem in the architecture or the query language? The two are not interchangeable, though frequently confused. Some answers can be found in the following articles, which represent a progression of ideas on this very relevant topic, based on various articles published in Nati Shalom’s blog: http://natishalom.typepad.com Should Web Apps "Just Say No" to SQL? Pros and Cons of Non-SQL Patterns This paper briefly reviews what is driving the trend of adopting alternatives to the traditional SQL DB, survey alternative approaches, and discuss not only their benefits but also the risks and caveats for real-life web applications.
As companies deal with ever larger amounts of data and increasingly demanding workloads, a new class of databases has taken hold. Dubbed “NoSQL”, these databases trade some of the features used by traditional relational databases in exchange for increased performance and/or partition tolerance. But as NoSQL solutions have proliferated and differentiated themselves (into key-value stores, document databases, graph databases, and “NewSQL”), trying to evaluate the database landscape for a particular class of problem becomes more and more difficult. In this paper we attempt to answer this question for one specific, but critical, class of functionality – applications that need the highest possible raw performance for a reliable storage engine. There have been a few attempts to provide standardized tools to measure performance or other characteristics, but these have been hobbled by the lack of a clear mandate on exactly what they’re testing, plus an inability to measure load at the highest volumes. In addition, there is an implicit tradeoff between the consistency and durability requirements of an application and the maximum throughput that can be processed. What is needed is not an attempt to quantify every NoSQL solution into one artificial bucket, but a more systemic analysis of how some of these databases can achieve under assumptions that mirror real-world application needs. We attempted to provide a comprehensive answer to one specific set of use cases for NoSQL databases -- consumer-facing applications which require extremely high throughput and low latency, and whose information can be represented using a key-value schema. In particular, we look at two common scenarios.
In this paper, I describe some of the recent developments in the database management area, in particular the NoSQL phenomenon and the hoopla associated with it. The goal of the paper is not to do an exhaustive survey of NoSQL systems. The aim is to do a broad brush analysis of what these developments mean - the good and the bad aspects! Based on my more than three decades of database systems work in the research and product arenas, I will outline what are many of the pitfalls to avoid since there is currently a mad rush to develop and adopt a plethora of NoSQL systems in a segment of the IT population, including the research community. In rushing to develop these systems to overcome some of the shortcomings of the relational systems, many good principles of the latter, which go beyond the relational model and the SQL language, have been left by the wayside. Now many of the features that were initially discarded as unnecessary in the NoSQL systems are being brought in, but unfortunately in ad hoc ways. Hopefully, the lessons learnt over three decades with relational and other systems would not go to waste and we wouldn’t let history repeat itself with respect to simple minded approaches leading to enormous pain later on for developers as well as users of the NoSQL systems! Caveat: What I express in this paper are my personal opinions and they do not necessarily reflect the opinions of my employer.
PostgreSQL as a Schemaless Database. Christophe Pettus PostgreSQL Experts, Inc. OSCON 2013 Welcome! • I’m Christophe. • PostgreSQL person since 1997. • Consultant with PostgreSQL Experts, Inc. • email@example.com • thebuild.com • @xof on Twitter. What’s on the menu? • What is a schemaless database? • How can you use PostgreSQL to store schemaless data? • How does do the various schemaless options perform? A note on NoSQL. • Worst. Term. Ever. • It’s true that all modern schemaless databases do not use SQL, but… • Neither did Postgres before it became PostgreSQL. (Remember QUEL?) • The deﬁning characteristic is the lack of a ﬁxed schema. Schematic. • A schema is a ﬁxed (although mutable over time) deﬁnition of the data. • Database to schema (unfortunate term) to table to ﬁeld/column/attribute. • Individual ﬁelds can be optional (NULL). • Adding new columns requires a schema change.
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