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Search Results For Train Simulator

You can use some or all of the search categories to find users and groups. If you select multiple categories, the AND operator is used. Any users or groups must match both values to be returned in the results (which is virtually impossible if you use the value All in multiple categories).

Search results for train simulator

The most popular online service for finding Shinkansen train timetables online among foreign tourists is Hyperdia, an online tool available on the web and also in apps for Android and iOS, largely thanks to its flexible search options and availability in English.

Cochrane Reviews take a systematic and comprehensive approach to identifying studies that meet the eligibility criteria for the review. This chapter outlines some general issues in searching for studies; describes the main sources of potential studies; and discusses how to plan the search process, design and carry out search strategies, manage references found during the search process, correctly document the search process and select studies from the search results.

Time and budget restraints require the review team to balance the thoroughness of the search with efficiency in the use of time and funds. The best way of achieving this balance is to be aware of, and try to minimize, the biases such as publication bias and language bias that can result from restricting searches in different ways (see Chapter 8 and Chapter 13 for further guidance on assessing these biases). Unlike for tasks such as study selection or data extraction, it is not considered necessary (or even desirable) for two people to conduct independent searches in parallel. It is strongly recommended, however, that all search strategies should be peer reviewed, before being run, by a suitably qualified and experienced medical/healthcare librarian or information specialist (see Section 4.4.8).

Many of the records in CENTRAL have been identified through systematic searches of MEDLINE, Embase, CINAHL Plus, Australian Index Medicus, KoreaMed, and the trial records available through the WHO International Clinical Trials Registry Portal (see online Technical Supplement). CENTRAL, however, also includes citations to reports of randomized trials that are not indexed in MEDLINE, Embase or other bibliographic databases; citations published in many languages; and citations that are available only in conference proceedings or other sources that are difficult to access. It also includes records from trials registers and trials results registers beyond and the WHO portal.

Cochrane Reviews of interventions should search relevant trials registers and repositories of results (see MECIR Box 4.3.d). A recent study suggested that trials registers are an important source for identifying additional randomized trials (Baudard et al 2017). A recent audit by Cochrane investigators showed that the majority of Cochrane Reviews do comply with this standard (Berber et al 2019). Although there are many other trials registers, and the WHO International Clinical Trials Registry Platform (ICTRP) portal (Pansieri et al 2017) are considered to be the most important for searching to identify studies for a systematic review. Research has shown that even though is included in the WHO ICTRP Search Portal, not all records could be successfully retrieved via searches of the ICTRP Search Portal (Glanville et al 2014, Knelangen et al 2018). The extent to which this might still be the case with the new ICTRP interface released in its final version in June 2021 (see online Technical Supplement) remains to be ascertained. Therefore, the current guidance that it is not sufficient to search the ICTRP alone still stands, pending further research. A recent study reviewed the search interfaces of the EU Clinical Trials Register (EUCTR), and the WHO ICTRP and offers further insights into how to search these resources (Cooper et al 2021a). Guidance for searching these and other trials registers is provided in the online Technical Supplement.

A number of organizations, including Cochrane, recommend searching regulatory agency sources and clinical study reports. These include the Agency for Healthcare Research and Quality (AHRQ) in the US, the Institute for Quality and Efficiency in Health Care (IQWiG) in Germany, and the Institute of Medicine in the US (Institute of Medicine 2011, Agency for Healthcare Research and Quality 2014, Institute for Quality and Efficiency in Health Care 2020). Potentially relevant regulatory agency sources include the EU Clinical Trials Register, Drugs@FDA and OpenTrialsFDA. Details of these are provided in the online Technical Supplement. Clinical study reports (CSRs) are the reports of clinical trials providing detailed information on the methods and results of clinical trials submitted in support of marketing authorization applications. In late 2010, the European Medicines Agency (EMA) began releasing CSRs (on request) under their Policy 0043. In October 2016, they began to release CSRs under their Policy 0070. The policy applies only to documents received since 1 January 2015. The terms of use for access are based on the purposes to which the clinical data will be put. Further details of this and other resources are available in the online Technical Supplement.

Developing a search is often an iterative and exploratory process. It involves exploring trade-offs between search terms and assessing their overall impact on the sensitivity and precision of the search. It is often difficult to decide in a scientific or objective way when a search is complete and search strategy development can stop. The ability to decide when to stop typically develops through experience of developing many strategies. Suggestions for stopping rules have been made around the retrieval of new records, for example to stop if adding in a series of new terms to a database search strategy yields no new relevant records, or if precision falls below a particular cut-off point (Chilcott et al 2003). Stopping might also be appropriate when the removal of terms or concepts results in missing relevant records. Another consideration is the amount of evidence that has already accrued: in topics where evidence is scarce, authors might need to be more cautious about deciding when to stop searching. Although many methods have been described to assist with deciding when to stop developing the search, there has been little formal evaluation of the approaches (Booth 2010, Arber and Wood 2021).

Details about contacting experts or manufacturers, searching reference lists, scanning websites, and decisions about search iterations can be produced as an appendix in the final document and used for future updates. The purpose of search documentation is transparency, internal assessment, and reference for any future update. It is important to plan how to record searching of sources other than databases since some activities (contacting experts, reference list searching, and forward citation searching) will occur later on in the review process after the database results have been screened (Rader et al 2014). The searcher should record any correspondence on key decisions and report a summary of this correspondence alongside the search strategy in a search narrative. The narrative describes the major decisions that shaped the strategy and can give a peer reviewer an insight into the rationale for the search approach (Craven and Levay 2011). A worked example of a search narrative is available (Cooper et al 2018b).

It is particularly important to save locally or file print copies of any information found on the Internet, such as information about ongoing and/or unpublished trials, as this information may no longer be accessible at the time the review is written. Local copies should be stored in a structured way to allow retrieval when needed. There are also web-based tools which archive webpage content for future reference, such as WebCite (Eysenbach and Trudel 2005). The results of web searches will not be reproducible to the same extent as bibliographic database searches because web content and search engine algorithms frequently change, and search results can differ between users due to a general move towards localization and personalization (Cooper et al 2021b). It is still important, however, to document the search process to ensure that the methods used can be transparently reported (Briscoe 2018). In cases where a search engine retrieves more results than it is practical to screen in full (it is rarely practical to search thousands of web results, as the precision of web searches is likely to be relatively low), the number of results that are documented and reported should be the number that were screened rather than the total number (Dellavalle et al 2003, Bramer 2016).

Hartling L, Featherstone R, Nuspl M, Shave K, Dryden DM, Vandermeer B. The contribution of databases to the results of systematic reviews: a cross-sectional study. BMC Medical Research Methodology 2016; 16: 127.

Hartling L, Featherstone R, Nuspl M, Shave K, Dryden DM, Vandermeer B. Grey literature in systematic reviews: a cross-sectional study of the contribution of non-English reports, unpublished studies and dissertations to the results of meta-analyses in child-relevant reviews. BMC Medical Research Methodology 2017; 17: 64.

Tap Add Stop (below Directions), use the search field or a recent search result to find and select a place to stop, then tap Add (in the list of search results) or Add Stop (in the place card for a search result).

  • This site features information about discrete event system modeling and simulation. It includes discussions on descriptive simulation modeling, programming commands, techniques for sensitivity estimation, optimization and goal-seeking by simulation, and what-if analysis.Advancements in computing power, availability of PC-based modeling and simulation, and efficient computational methodology are allowing leading-edge of prescriptive simulation modeling such as optimization to pursue investigations in systems analysis, design, and control processes that were previously beyond reach of the modelers and decision makers.Professor Hossein Arsham To search the site, try Edit Find in page [Ctrl + f]. Enter a word or phrase in the dialogue box, e.g. "optimization" or "sensitivity" If the first appearance of the word/phrase is not what you are looking for, try Find Next.MENUIntroduction & SummaryStatistics and Probability for SimulationTopics in Descriptive Simulation ModelingTechniques for Sensitivity EstimationSimulation-based Optimization TechniquesMetamodeling and the Goal seeking Problems "What-if" Analysis TechniquesCompanion Sites:JavaScript E-labs Learning Objects Statistics Excel For Statistical Data Analysis Topics in Statistical Data Analysis Time Series Analysis Computers and Computational Statistics Probabilistic ModelingProbability and Statistics ResourcesOptimization ResourcesSimulation ResourcesIntroduction & SummaryStatistics and Probability for SimulationStatistics for Correlated DataWhat Is Central Limit Theorem?What Is a Least Squares Model?ANOVA: Analysis of VarianceExponential Density FunctionPoisson Process Goodness-of-Fit for PoissonUniform Density Function Random Number GeneratorsTest for Random Number Generators Some Useful SPSS Commands References & Further ReadingsTopics in Descriptive Simulation Modeling Modeling & Simulation Development of Systems SimulationA Classification of Stochastic ProcessesSimulation Output Data and Stochastic ProcessesTechniques for the Steady State SimulationDetermination of the Warm-up PeriodDetermination of the Desirable Number of Simulation RunsDetermination of Simulation Runs Simulation Software Selection Animation in Systems SimulationSIMSCRIPT II.5 System Dynamics and Discrete Event SimulationWhat Is Social Simulation?What Is Web-based Simulation?Parallel and Distributed Simulation References & Further ReadingsTechniques for Sensitivity EstimationIntroduction Applications of sensitivity information Finite difference approximationSimultaneous perturbation methodsPerturbation analysisScore function methodsHarmonic analysisConclusions & Further Readings Simulation-based Optimization TechniquesIntroductionDeterministic search techniquesHeuristic search technique

  • Complete enumeration and random choice

  • Response surface search

  • Pattern search techniquesConjugate direction search

  • Steepest ascent (descent)

  • Tabu search technique

  • Hooke and Jeeves type techniques

  • Simplex-based techniques

  • Probabilistic search techniques Random search

  • Pure adaptive and hit-and-run search

  • Evolutionary Techniques Simulated annealing

  • Genetic techniques

  • A short comparison

  • References and Further Readings

  • Stochastic approximation techniques Kiefer-Wolfowitz type techniques

  • Robbins-Monro type techniques

Gradient surface methodPost-solution analysisRare Event SimulationConclusions & Further Readings Metamodeling and the Goal seeking ProblemsIntroductionMetamodelingGoal seeking ProblemReferences and Further Readings "What-if" Analysis TechniquesIntroductionLikelihood Ratio (LR) MethodExponential Tangential in Expectation MethodTaylor Expansion of Response FunctionInterpolation Techniques Conclusions & Further ReadingsIntroduction & SummaryComputer system users, administrators, and designers usually have a goal of highest performance at lowest cost. Modeling and simulation of system design trade off is good preparation for design and engineering decisions in real world jobs. In this Web site we study computer systems modeling and simulation. We need a proper knowledge of both the techniques of simulation modeling and the simulated systems themselves.The scenario described above is but one situation where computer simulation can be effectively used. In addition to its use as a tool to better understand and optimize performance and/or reliability of systems, simulation is also extensively used to verify the correctness of designs. Most if not all digital integrated circuits manufactured today are first extensively simulated before they are manufactured to identify and correct design errors. Simulation early in the design cycle is important because the cost to repair mistakes increases dramatically the later in the product life cycle that the error is detected. Another important application of simulation is in developing "virtual environments" , e.g., for training. Analogous to the holodeck in the popular science-fiction television program Star Trek, simulations generate dynamic environments with which users can interact "as if they were really there." Such simulations are used extensively today to train military personnel for battlefield situations, at a fraction of the cost of running exercises involving real tanks, aircraft, etc.Dynamic modeling in organizations is the collective ability to understand the implications of change over time. This skill lies at the heart of successful strategic decision process. The availability of effective visual modeling and simulation enables the analyst and the decision-maker to boost their dynamic decision by rehearsing strategy to avoid hidden pitfalls.System Simulation is the mimicking of the operation of a real system, such as the day-to-day operation of a bank, or the value of a stock portfolio over a time period, or the running of an assembly line in a factory, or the staff assignment of a hospital or a security company, in a computer. Instead of building extensive mathematical models by experts, the readily available simulation software has made it possible to model and analyze the operation of a real system by non-experts, who are managers but not programmers. A simulation is the execution of a model, represented by a computer program that gives information about the system being investigated. The simulation approach of analyzing a model is opposed to the analytical approach, where the method of analyzing the system is purely theoretical. As this approach is more reliable, the simulation approach gives more flexibility and convenience. The activities of the model consist of events, which are activated at certain points in time and in this way affect the overall state of the system. The points in time that an event is activated are randomized, so no input from outside the system is required. Events exist autonomously and they are discrete so between the execution of two events nothing happens. The SIMSCRIPT provides a process-based approach of writing a simulation program. With this approach, the components of the program consist of entities, which combine several related events into one process. In the field of simulation, the concept of "principle of computational equivalence" has beneficial implications for the decision-maker. Simulated experimentation accelerates and replaces effectively the "wait and see" anxieties in discovering new insight and explanations of future behavior of the real system.Consider the following scenario. You are the designer of a new switch for asynchronous transfer mode (ATM) networks, a new switching technology that has appeared on the marketplace in recent years. In order to help ensure the success of your product in this is a highly competitive field, it is important that you design the switch to yield the highest possible performance while maintaining a reasonable manufacturing cost. How much memory should be built into the switch? Should the memory be associated with incoming communication links to buffer messages as they arrive, or should it be associated with outgoing links to hold messages competing to use the same link? Moreover, what is the best organization of hardware components within the switch? These are but a few of the questions that you must answer in coming up with a design. With the integration of artificial intelligence, agents and other modeling techniques, simulation has become an effective and appropriate decision support for the managers. By combining the emerging science of complexity with newly popularized simulation technology, the PricewaterhouseCoopers, Emergent Solutions Group builds a software that allows senior management to safely play out "what if" scenarios in artificial worlds. For example, in a consumer retail environment it can be used to find out how the roles of consumers and employees can be simulated to achieve peak performance.Statistics for Correlated DataWe concern ourselves with n realizations that are related to time, that is having n correlated observations; the estimate of the mean is given by 041b061a72


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