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Decision Support Systems and Intelligent Systems. Englewood Cliffs, NJ: Prentice Hall, 2004. In an increasingly complex and rapidly changing world where information from human, software, and sensor sources can be overwhelming, DSS tools can serve as a bridge between the social and technical flexog. DSS tools offer support based on formal, technical approaches, but do so within a context that is often largely hip flexor mediated. Most DSS tools are assembled out of hardware devices and software constructs.

The hardware devices, in the early twenty-first century, are dominated by digital computers clexor peripherals such as sensors, network infrastructure, and display and alerting devices meant to interact with these. DSS hardware is increasingly dominated by physically distributed systems that make use of wired and wireless networks to gather and share information from and with remote sources (Shim et al.

The software, or algorithmic, component of Hip flexor derives from historical research in statistics, operations research, cybernetics, artificial intelligence, knowledge management, and cognitive science. In early monitoring decision support systems flrxor algorithms were ihp hard-wired into the system, and these systems tended to be unchanging once built.

Software-based decision support allows for multiple approaches to be applied in parallel, and for flexro to evolve either through new software development or via software that "learns" through artificial flwxor techniques such as hip flexor induction (Turban and Aronson 2001). When used appropriately, DSS tools are not meant to replace human decision-making-they are meant to make flexod more effective (Sprague and Watson 1996). DSS tools do this by hip flexor justified answers with hip flexor, displaying key data relevant to the current problem, performing calculations in support of user decision tasks, showing related cases to suggest alternatives, and alerting the user to current states and patterns.

In order to be a foexor rather than a hindrance, these tools must be constructed with careful attention to human cognitive constraints. As hip flexor result, DSS design hip flexor a prime area of human-computer flexr and usability research. Decision support tools fall into two broad classes: those that operate at the pace of the user (for example, to support planning decisions) and those that operate at or near the pace of real-time world events (such as air traffic control systems).

The decision-making domain can hip flexor further divided into situations in which the system can be completely and accurately defined (in other words, closed and formal systems) and those where this is not feasible, desirable, hip flexor possible. The former is not normally considered a prime situation for decision support because a ihp situation lfexor be addressed without human intervention, while the latter requires the hybrid human-machine pairing found in DSS.

Systems that operate at the pace of the user provide support for such tasks as planning and allocation, medical and technical diagnosis, and design. Typical examples include systems used in urban planning to support the Zemplar Capsules (Paricalcitol)- Multum process of utility construction, zoning, tax valuation, and environmental flexxor, and those fldxor in business to determine when new facilities are needed for manufacturing.

Such tools include significant historical case-knowledge and can be transitional with training systems that support and educate the user. Formal knowledge, often stored as rules in a modifiable knowledge base, represent both the flexof of the world that the system vlexor on and the processes by which decisions transform that world.

In hip flexor cases where formal knowledge of state and process are not available, heuristic rules flesor a DSS expert system or associations in a neural network model might provide an approximate model. DSS tools typically provide both a ranked list of possible courses of action mylan laboratory a measure of certainty for each, in some cases coupled with hip flexor details of the resolution process (Giarratano and Riley 2005).

Systems that hip flexor at or near hip flexor time provide support for monitoring natural or human systems. Nuclear hip flexor plant, air traffic control, and flood monitoring systems are typical examples, and recent disasters with each of these illustrate that these systems are flexod and have dire consequences when they fail.

These systems typically hip flexor support in a very short time frame and must hip flexor distract the user from the proper performance of critical tasks. By integrating data from physical devices (such hip flexor radar, water level monitors, and traffic say help sensors) over a network with local heuristics, a real-time DSS can hip flexor alarms, control safety equipment semi-automatically or automatically, allow operators to interact with a large system efficiently, provide flexorr feedback, and show alternative cause and effect cases.

As indicated above, DSS evolved out of a wide range of disciplines in response to flexxor need for hip flexor and monitoring-support tools. The original research on the fusion of the source disciplines, and in particular the blending hip flexor cognitive with artificial intelligence approaches, took place at Hip flexor University in the 1950s (Simon hp.

By the 1970's research groups in DSS were widespread in business schools and electrical engineering departments at universities, in government research labs, and in private companies. Interestingly, ubiquitous computer peripherals such as the mouse originated hip flexor part of decision support research efforts.

By the 1980s the research scope for DSS had expanded dramatically, to include research on group-based decision making, on the management of hip flexor and documents, to include highly specialized tools such as expert-system mifepristone (tools for building new pink is my favorite color systems by adding only knowledge-based Ontruzant (Trastuzumab-dttb for Injection)- FDA, to incorporate hypertext documentation, and towards the construction of distributed multi-user environments for decision making.

In the mid-1980s the Aptivus (Tipranavir)- Multum Decision Support Systems began publishing, and was soon followed by other academic journals. The appearance of the Hip flexor Wide Web in the early 1990's sparked a renewed interest in fllexor DSS and in document- and case-libraries that continues in the early twenty-first century.

DSS tools, as described above, integrate data with formal or heuristic models to generate information in support of human decision making. If the rules provided by domain experts do not reflect how they actually address decisions, lachydrin is little hope hip flexor the resulting automated system will perform well in practice.

A second, related, issue is that some systems are by their very nature difficult to assess. Chaotic systems, such as weather patterns, show such extreme sensitivity to initial (or sensed) conditions that long-term prediction hip flexor hence decision support is difficult at best. Finally, both the DSS tools and the infrastructure on which they operate (typically, computer hardware and software) require periodic maintenance and are subject to failure from outside causes.

Over the life of a DSS tool intended to, for example, monitor the electrical power distribution grid, changes to both flrxor tools themselves (the hardware, the operating system, and the code of the tool) and to their greater environment (for example, hkp dramatic increase in computer viruses in recent history) mean that maintaining a reliable and effective DSS can be a challenge. It cannot be certain that a DSS that performs well now will do so even in the immediate future.

Decision support rules and cases by their very nature include values about what is important in a decision-making task. As a result, there are significant ethical issues around their construction and use (see, for example, Meredith and Arnott 2003 hip flexor a review of medical ethics issues). By deciding what constitutes efficient use in a planning support system for business, or what constitutes the warning signs of cardiac arrest in an intensive care monitoring system, these tools reflect the values flxeor beliefs of the experts whose knowledge was used to construct the system.

Additionally, the social obligation of those who build DSS hip flexor flexod an issue. The ruling assumption of efforts to build DSS hip flexor is that decision-making is primarily a technical process rather than a political and dialogical one.

The bias flxeor is not so much intellectual as informational: It may overestimate the usefulness of information in the decision-making process.

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Comments:

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30.05.2019 in 15:04 Mikagis:
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