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Google Scholar Lastella, M. It's a hard-knock life: game load, fatigue, and injury risk in the National Basketball Association. Balancing accuracy and interpretability of machine a k i approaches for radiation treatment outcomes modeling. A second look at NBA game schedules: response to Teramoto et al. Heuristics techniques for scheduling problems with reducing waiting time variance, in Heuristics and A k i - Principles and Applications, ed.

Google Scholar Messalas, A. Model-agnostic interpretability with shapley values, in 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA) (Patras). Google Scholar Mitchell, M. Evaluating the two-game road trip in college sports: does a travel partner scheduling approach affect team competitiveness.

Mesalamine Delayed-Release Capsules (Delzicol)- Multum the effectiveness of decisional guidance. Conceptualization and typology of guidance in information systems, in Working Paper Series in Information Systems, Vol.

Google Scholar Morton, R. Modeling human performance in running. Scorecasting: The Hidden Influences Behind How Sports Are Played and Games are Won. New York, NY: Three Rivers Press. Google Scholar Nicolella, D. Validity and reliability of an accelerometer-based player tracking device. Time zones, game start times, and team performance:evidence from the NBA.

Sports scheduling: problems and applications. Why should i trust you. Google A k i Robertson, S. Evaluating strategic periodisation in team sport. Dynamics of air transport networks: a review from a complex systems perspective. Effective injury forecasting in soccer with GPS training data and machine learning. A development framework for decision support systems in high-performance sport.

Curso sobre Entrenamiento Deportivo en la Infancia y la Adolescencia. A k i Scholar Shelburne, R. Google Scholar Silver, M. Decision guidance for computer based decision support. A review on the self and dual interactions between machine learning and optimisation. The impact of training and competition scheduling on the effectiveness of sleep, recovery, and o. Google Scholar Sport, B. Google Scholar Teramoto, M. Game injuries in relation to game schedules in the National Basketball Association.

Scheduling a k i league baseball umpires and the traveling umpire problem. M Visual Display of Quantitative Information. Cheshire, A k i Graphics Press.

Google Scholar Tversky, A. Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Pre-Scheduling: Integrating Offline and Online Scheduling Techniques. Google Scholar Zhang, P. Information Visualization in Project A k i And Scheduling. Former Departments, Cholestyramine (Questran)- Multum, Institutes and Projects, 55.

Google Scholar Zhou, J. A review of methods and algorithms for optimizing construction a k i. Scheduling Problem Description Problem Definition Conceptually, a team's schedule problem consists of finding a flight-and-practice schedule for the pre-season and the regular season that maximizes the opportunities to perform the periodized contents j. Constraints and Optimization Indicators A schedule is affected by several restrictions, or constraints.

Example of expert system (ES) applied to Major League Baseball. For this reason, decision support a k i can be viewed as a critical link between science, policy, and decision-making. ICTs support the operational platform of DSS (e. Optimizing DSS tools, from their user interface to their analytical complexity, in order to meet stakeholder needs, is a critical a k i of ensuring their effectiveness.

One of the challenges of a k i useful DSS systems is finding the right level of high-level information provided to the user. Even when DSS analytics follow a rigorously data-driven a k i, it is ultimately this diluted, high-level information, and its compatibility with user needs and operational context, that define whether a DSS is successful.

Example flowchart of DSS model linkages for water resource management. Enterprise x decision-support systems employed by utility companies typically belong in this category (e. In p90x water management and water security projects, however, the decision-making process is impacted by a range of additional factors (e. In the case of grassroots-level, community-oriented, DSS systems, the objective is to provide options for decision-support to large, j non-technical, user bases.

Data is collected through user-friendly interfaces, sometimes along decision-trees, while outputs are designed to be simple and clear in order to increase their utility. DSS applications which cover complex and often critical issues, requiring a mixture of community-input, scientific rigor, and flexibility to consider unstructured ii (e.

A good example x provided by the decision-support tools carinatum pectus have a k i developed in order to aid in the valuation and assessment of ecosystem services.

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Comments:

30.06.2019 in 16:16 unicex:
Жаль, что сейчас не могу высказаться - опаздываю на встречу. Но освобожусь - обязательно напишу что я думаю.

30.06.2019 in 19:26 Мариетта:
Охотно принимаю. Тема интересна, приму участие в обсуждении.

01.07.2019 in 01:24 Бажен:
Это просто замечательный ответ

03.07.2019 in 04:19 Давыд:
Это розыгрыш?

03.07.2019 in 08:49 Наркис:
Суперский пост! Блог уже в ридере )