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This paper describes the benefits of storing Oracle NoSQL Database data on Fusion’s ioDrive2 products. Oracle and Fusion-io partnered to test, validate, and deliver extreme high performance big data solutions for real-time applications. The superior performance of Fusion’s ioDrive2 complements the scalability, reliability, and simplicity of Oracle NoSQL Database, dramatically improving throughput and response times for serving key-value data. The combination of Oracle NoSQL Databases and ioMemory provide a compelling and cost-effective solution in a variety of scenarios. Results of testing showed that using an ioDrive2 for data delivered nearly 30 times more operations per second than a 300GB 10k SAS disk on a 90 percent read and 10 percent write workload and nearly eight times more operations per second on a 50 percent read and 50 percent write workload. Equally impressive, an ioDrive2 reduced latency over 700 percent (seven times) on inserts in a 90 percent read and 10 percent write workload and over 5800 percent (58 times) on reads in a 50 percent read and 50 percent write workload. WHAT IS BIG DATA? Big Data is an informal term that encompasses all sorts of data, including Web logs, sensor data, tweets, blogs, user reviews, and SMS messages. It is characterized by: high volume of hundreds of terabytes or more; wide data variety with no inherent structure (one row looks very different from another); and high velocity, on the order of hundreds of thousands of operations per second. Often, big data is processed using purpose-built software designed to address a specific data processing requirement. This category of big data processing solutions is generally referred to as NoSQL (not SQL or Not Only SQL). Although it is possible to process big data using traditional SQL-based products and solutions, NoSQL databases provide a more cost-effective and horizontally scalable alternative. NoSQL databases complement SQL-based solutions, providing significant new business advantages to the enterprise. Recently, there has been a huge surge of interest in big data processing solutions. As enterprises have embraced big data processing for business benefit, open source and commercial vendors have responded by providing a variety of solutions aimed at addressing specific big data processing needs. In October 2011, Oracle announced a suite of complementary products and technologies that provide a complete and comprehensive solution to address the big data processing needs of the market. Big data processing falls into two major categories: interactive processing and batch processing. In most big data processing applications, both kinds of data processing are required. Oracle NoSQL Database (NoSQL DB for short), also released in October 2011, is a scalable, highly available key-value store that can be used to acquire and manage vast amounts of...
The instructor's expectations for students during the semester: The students will be responsible for all materials covered in each lab and assigned in the book and lab reports. The students are expected to read the appropriate materials in the text before each laboratory. The key to success is hard work. Learning Outcomes: The overall goal of this course is for students to understand the basic principles of biochemistry laboratories. To this end, the following major learning outcomes shall apply: Students are expected to understand basic techniques in biochemistry. A necessary prerequisite for professional performance in the laboratory is preparation. Since our schedules are very tight for every experiment, you have to make sure you have read the experimental and instrumental instructions before coming to the class. Please bring an outline how to make your solutions and perform your experiments to the class. We have included a schedule in this manual. You are expected to take all classes. Otherwise, you will be advised to drop the class. However, some special emergency circumstances, for example illness when accompanied with a signed letter from a medical doctor stating explicitly that you are not able to take the class on that day for your own health reasons, will be considered. If you are not able to attend the class, please inform the instructor immediately so that an alternative can be arranged. Since you are registering a formal class at FIU, it is your benefit to arrive prior to 2:00 PM. You are allowed to be late for two times. After the third time late, you will be advised to drop this class. You will not allow attending the class after 2:30 PM. please come to the class on time! All experimental results should be documented in your laboratory notebook, which will provide a solid basis for writing your lab report. The original data sheets must accompany your reports.
WELCOME TO THE BIOCHEMISTRY LABORATORY! This Biochemistry laboratory seeks to model work performed in a biochemical research laboratory. The course will guide you through basic lab techniques, investigations into DNA and enzyme kinetics, an intensive purification and characterization of an unreported protein, and will culminate in a formal research paper in the format of an article published in Biochemistry. Module 1 is concerned with basic lab skills. In these labs, we will learn how scientists think and write about biochemistry and perform experiments. We will also learn to accurately and precisely measure small volume of liquid while avoiding sample contamination. Lastly, we will learn to compute and create buffer solutions—a cornerstone of biochemistry. Module 2 will allow us to purify the protein cytochrome c from a yeast species (Saccharomyces cerevisiae) using various fractionation techniques including homogenization, centrifugation, and column chromatography. We will characterize our products using biochemical methods including gel electrophoresis, UV-Vis spectroscopy, and electrochemistry. Using modeling software on the computer, the structure and function of model, comparison cytochrome c proteins will be investigated. As a result of this project, we will determine the molecular weight, the approximate number and type of aromatic residues, characteristic UV-Vis spectra, and denaturation/renaturation properties of cyctochrome c. Module 3 looks into the processes used to isolate, purify, amplify, and characterize DNA. We will isolate and purify DNA from a bacterial source, and design and then use then use the polymerase chain reaction (PCR) to amplify a DNA region of interest to ascertain the nature of the DNA we purified. Finally, we will perform in silico studies of DNA cloning, followed by DNA restriction and ligation for transformation into a bacterial expression system—molecular cloning. Module 4 is focused on enzyme kinetics, the measurement of the extent and mechanism by which enzymes catalyze biological reactions. We will investigate these processes by looking at the activity of tyrosinase found in mushrooms, which catalyze oxidation of various substrates. We will also investigate the effect of enzyme inhibitors of these reactions. The emphasis of the lab is on learning to perform complex biochemical techniques, as well as on analyzing and interpreting data and using graphing programs. Lab instructions and report expectations are explained in the pages that follow.
Overview and the Question of “Where to Start?” The increased interest in and importance of enterprise risk management is being driven by many powerful forces. Most importantly, it is driven by the need for companies to manage risks effectively in order to sustain operations and achieve their business objectives. Other forces also come into play, including rating agency reviews, government regulations, expanded proxy disclosures, and calls by shareholders and governance reform proponents for improving the way risks are managed by organizations. Any entity that is currently operational has some form of risk management activities in place. However, these risk management activities are often ad hoc, informal and uncoordinated. And, they are often focused on operational or compliance-related risks and fail to focus systematically on strategic and emerging risks, which are most likely to affect an organization’s success. As a result, they fall short of constituting a complete, robust risk management process as defined by COSO (See definition of ERM below). In addition, existing risk management activities often lack transparency. Transparency about how enterprise-wide risks are managed is increasingly being sought by directors and senior management, as well as various external parties seeking to understand an organization’s risk management activities. What’s more, existing risk management processes often are not providing boards and senior management with an enterprise-wide view of risks, especially, emerging risks. Unfortunately, many organizational leaders are struggling with how to begin in their efforts to obtain strategic benefit from a more robust enterprise-wide approach to risk management. Enterprise risk management is a process, effected by an entity’s board of directors, management, and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within the risk appetite, to provide reasonable assurance regarding the achievement of entity objectives...
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Today, analysis and design of business processes are the major tasks of business engineering [Scheer (1994), Österle (1997), Hammer et al. (1993), Davenport (1993)]. In research as well as in practice, the Architecture of integrated Information Systems (ARIS) [Scheer (1992)] is accepted as a standard framework for business process (re-)engineering. It supports the whole process management life cycle consisting of process design, process management, process workflow and process application implementation [Scheer (1996)]. The Unified Modeling Language (UML) [Rational Software (editor) (1997)] is a common standard for object-oriented modeling. The UML is derived of a shared set of commonly accepted concepts which have successfully been proven in the modeling of large and complex systems, especially software systems. With the UML extension for business modeling, a first object-oriented UML terminology has been defined for the domain of business modeling. ARIS as well as UML are based on integrated meta models supported by several modeling tools. The core business modeling concepts of both methodologies will first be introduced and compared afterwards. The method of Event-driven Process Chains (EPC) [Keller et al. (1992), Nüttgens (1997)] has been developed within the framework of ARIS in order to model business processes. In the EPC model, a process consists of sequences of events triggering business functions, which are themselves the results of other functions apart from initial events triggering the whole process. By introducing boolean operators (''and'', ''or'', ''exclusive or''), the event-driven control structure can be expanded to a complex control flow illustrating business relevant decisions. This basic model of the EPC can be extended by further semantic components of description. The illustration of data flows, responsibility of organization units and the use of IT systems are examples for such an extension (see figure 1). Furthermore, on the basis of formal descriptions of the EPC method, tool-supported concepts for analysis and simulation are being developed. The approach of Langner/Schneider/Wehler [Langner et al. (1997)] aims at the translation of EPC models into petri networks and at the algorithmic verification of the resulting networks. In contrast to this, the approaches of Rump [Rump (1997)] and of Keller/Teufel [Keller and Teufel (1997)] are based on a formal description of the EPC.
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The evolution of Matrix Structural Analysis (MSA) from 1930 through 1970 is outlined. Hightlighted are major contributions by Collar and Duncan, Argyris, and Turner, which shaped this evolution. To enliven the narrative the outline is conﬁgured as a three-act play. Act I describes the pre-WWII formative period. Act II spans a period of confusion during which matrix methods assumed bewildering complexity in response to conﬂicting demands and restrictions. Act III outlines the cleanup and consolidation driven by the appearance of the Direct Stiffness Method, through which MSA completed morphing into the present implementation of the Finite Element Method. Keywords: matrix structural analysis; ﬁnite elements; history; displacement method; force method; direct stiffness method; duality Who ﬁrst wrote down a stiffness or ﬂexibility matrix? The question was posed in a 1995 paper . The educated guess was “somebody working in the aircraft industry of Britain or Germany, in the late 1920s or early 1930s.” Since then the writer has examined reports and publications of that time. These trace the origins of Matrix Structural Analysis to the aeroelasticity group of the National Physics Laboratory (NPL) at Teddington, a town that has now become a suburb of greater London. The present paper is an expansion of the historical vignettes in Section 4 of . It outlines the major steps in the evolution of MSA by highlighting the fundamental contributions of four individuals: Collar, Duncan, Argyris and Turner. These contributions are lumped into three milestones: Creation. Beginning in 1930 Collar and Duncan formulated discrete aeroelasticity in matrix form. The ﬁrst two journal papers on the topic appeared in 1934-35 [2,3] and the ﬁrst book, couthored with Frazer, in 1938 . The representation and terminology for discrete dynamical systems is essentially that used today. Uniﬁcation. In a series of journal articles appearing in 1954 and 1955  Argyris presented a formal uniﬁcation of Force and Displacement Methods using dual energy theorems. Although practical applications of the duality proved ephemeral, this work systematized the concept of assembly of structural system equations from elemental components. FEMinization. In 1959 Turner proposed  the Direct Stiffness Method (DSM) as an efﬁcient and general computer implementation of the then embryonic, and as yet unnamed, Finite Element Method.
The mission of the Insurance Division is to administer the Insurance Code for the protection of the insurancebuying public while supporting a positive business climate. We ensure the financial soundness of insurers, the availability and affordability of insurance, and the fair treatment of consumers by doing the following: • Licensing insurance companies and monitoring their solvency • Reviewing insurance products and premium rates for compliance • Licensing insurance producers (agents) and consultants • Resolving consumer complaints • Investigating and penalizing companies and producers (agents) for violations of insurance law • Monitoring the marketplace conduct of insurers and producers (agents) • Educating the public about insurance issues • Advocating reforms that protect the insurance-buying public Call us for help ■ Consumer Advocacy Unit — 503-947-7984 or 888-877-4894 (toll-free) You have the right to seek assistance from the Insurance Division at any time by filing a formal complaint against an insurance company or producer (agent). A copy of the complaint is sent to the insurance company. A response from the insurance company or producer (agent) must be received at the Insurance Division within 21 days. A consumer advocate will determine what further actions, if any, will be taken. The Insurance Division will forward a copy of the insurance company’s response to you. If a law has been broken, the matter may be referred to the Insurance Division’s Investigations Unit. ■ Financial Regulation Section — 503-947-7982 To find out if a company is authorized to sell insurance in Oregon, call our Financial Regulation Section or visit our website, insurance.oregon.gov, and click on “Information for Insurance Companies.” ■ Producer (Agent) Licensing Unit — 503-947-7981 To find out if your insurance producer (agent) is licensed to do business in Oregon, call our Producer Licensing Unit or visit our website, insurance.oregon.gov, and click on “Information for Insurance Companies,” then “Insurance Producer Search Page.”
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