Experimentation. (1) The Board and RAP recognize the need for experimentation and innovation in programs and techniques effecting unit members and agree to cooperate in the implementation thereof.
(2) Every effort will be made to provide such proposed programs to the President prior to the programs being submitted to the Board of Education for its consideration.
(3) Any program being studied by the District for possible implementation shall receive participation and discussion from a minimum of three (3) members of the bargaining unit, to be selected by the Association.
(4) The President of RAP or his/her designee shall be an ex officio member of the City School District Federal Projects Committee.
(5) The President of RAP shall be notified of any new school programs under consideration by a school based planning team which would impact upon unit members’ duties. The President, or designee, shall be invited to participate in discussions of such programs.
(6) The President of RAP shall be notified of any Central Office department reorganization that has potential to affect unit members.
Experimentation. The Board and the Association recognize the need for experimentation and innovation in instructional programs and techniques and agree to cooperate in the implementation thereof.
Experimentation. A simulator that allows to control both robot and human behaviors was developed. Here, positions and velocities can be controlled. Then, it is employed to generate trajectories of robot while approaching humans. The robot is manually controlled during different approaching scenarios. A set of demonstrations was performed in this experimental platform for the learning process. The path taken by the robot in different positions with different orientations can be seen in Figure 3. This represents the path followed by the robot in the human reference frame.
Experimentation. 5.1 Case a1: Adaptive traffic sampling and management
5.1.1 Performance objectives, and evaluation criteria Collected measurements can decide on how to tune the sampling rates inside the network. We consider for this purpose an important monitoring application, the estimation of the volume (in terms of number of packets or bytes) of some chosen network flows. A flow is a set of 5-tuple flows that share some common features as the source IP address prefix, the destination IP address prefix, the same ingress and egress routers inside the network, etc. At the limit, a flow can be one 5-tuple flow or even the entire network traffic. Given a set of flows to monitor, the machine learning engine should progressively tune the sampling rates in routers in such a way to minimize the global estimation error.
Experimentation. Adaptive management is strongly rooted in scientific experimentation. By specifically designing experiments into management actions, conclusions can be drawn that help develop better resource management decision making. Experimentation in Battle Creek is embodied in three ways, where experimentation (1) has been a component of adaptive management problem definition and solution development, (2) is embodied in the overall Adaptive Management program as envisioned in this document, and (3) may be conducted as part of individual Adaptive Management objectives considered under this plan within the established protocols.
Experimentation. Expedient provides a useful graphical interface to the users for managing their experiments. Through Expedient, a user can create a project (a container for experiments) by sending a request to the administrator. Once the project is created, the creator can add other users to the project so that they can also perform experiments in that project. The user can also add aggregate managers or RMs to the project, that is, the resources that will be available for the experiments. The experiments are created as slices. AGer creating a slice, a user can select from AMs provided by that project for use in experiment. Once included, the resources of these AMs are graphically displayed in Expedient along with the links between them. A new GUI section for the Expedient has been created in XXXXX project which provides information about the status of the components and the slice topology.
Experimentation. In order to accurately understand the role of hormone status and relative levels of progesterone-derived neurosteroids, it was important to have the mice on a normal estrous cycle. This cycle occurs on a shorter time scale than the 28- day human menstrual cycle. A typical cycle will last 4 days and pass through 4 stages, with diestrus experiencing sharp changes in hormone levels and estrus showing lower, more stable levels (Xxxxxxx et al., 1974). For the most part, when female mice are included in an experiment, their hormonal levels do not accurately mimic what they would be in nature. There is a phenomenon called the Xxx-Boot Effect, where females that are in same-sex housing will have suppressed estrous cycles (Xxx xxx Xxx, 1955). The Xxxxxxx Effect has to do with the initiation of ovulation and the regular cycling of ovarian hormones by the presence of male pheromones (Xxxxxxx et al., 1957). For this project, nest materials were taken out of a dirty male cage and placed in a cage that housed a group of females. Ovulation typically happens about a day after the cage is spiked with the male bedding. The females were allowed to naturally cycle for a week before testing began. Estrus females had their bedding spiked about 5-6 days prior to testing, whereas diestrus females had their bedding spiked about 7-8 days prior to testing. On the morning that testing was to take place, vaginal lavage samples were taken by flushing a small amount of saline into the vaginal opening to collect a sample of epithelial cells. The slides were examined to ensure the status of the females, before being allowed to dry prior to staining. Due to the stressful nature of the lavage samples, the males were also individually handled to equate the experiences of the two sexes. After the slides had dried completely, a vaginal cell staining protocol was followed from ScyTek (Papanicolaou Staining Protocol). The smears were then imaged and classified, based on cell cytology information and correlating hormone levels (XxXxxx et al., 2012). The females were then divided into two groups based on cell composition of the vaginal lavage samples and the correlating levels of hormones. Estrus females were classified if the majority of the cells in the vaginal lavage sample were cornified epithelial cells, stained pink; diestrus females were categorized by a predominance of leukocytes, stained dark brown. The first experiment that was carried out was to look at the anxiolytic effect of either o...
Experimentation. In the case of long-term experimentation, the Union shall receive prior notice thereof.
Experimentation. Innovation involves change, new ideas, experimentation, and some risk of failure. Experiments that will help us achieve environmental goals in better ways are worth pursuing when success is clearly defined, costs are reasonable, and environmental and public health protections are maintained.
1. The States and EPA should recognize the value of prudent risk-taking through experiments designed to achieve improved results.
2. The States and EPA should seek ways to make good ideas work, presuming that innovations to help meet environmental goals are worth our investment.
3. The States and EPA should carefully monitor and manage innovations to ensure that problems are immediately identified and remedied. Experimentation should be based on sound judgment, reasoning and common sense.
4. If a promising experiment encounters difficulties that likely can be corrected and that do not jeopardize environmental protection, project sponsors should be allowed to fix problems before the experiment is abandoned in favor of the traditional approach.
5. Experimentation does not include relaxing health or environmental standards or reducing protection of public health or the environment.
6. Experiments should be designed to test new approaches and as appropriate lessons learned should be used to improve the current system of environmental protection.
Experimentation. Mission-Oriented innovation policy should lead to extensive experimentations of possible solutions to the problem identified. This responds both to a logic of risk management (different solutions, with different levels of risk and reward, should be tried at the same time), and to a logic of more inclusive innovation policy (the whole EU community or researchers and innovators should potentially be involved in trying to find a solution to the problem). Experimentation could follow two tracks: • Track 1: Experimenting with new technologies/business models/delivery modes, and blending funding instruments and schemes to run experiments. This could happen on a “prize” basis, or on a more top-down selection of possible paths (e.g. technology roadmap), or both. For example, the replacement of general practitioners with online, constantly available bots could be subject to experimentation with a sample of patients, carefully selected; the same could happen for the procurement of local solutions to CO2 emissions or water draught; or the application of blockchain to electoral systems or land registries. At a more basic research stage, alternative therapies for Alzheimer could be developed and tested to have a chance to speed up scientific breakthrough (e.g. Repetitive Transcranial Magnetic Stimulation, or rTMS). The expectation is that most of these attempts will fail, and a few will lead to results. In terms of instruments, the expectation is that missions will be able to tap into various sources of funding, including research funds, EIC funds, EIB, InvestEU, structural and cohesion funds, national funds made available on a voluntary basis by Member States and even non-EU countries (in the spirit of “Open to the World”), and private funds (partnerships): the ability to blend different forms of funding shall be considered as essential to the skills and activity of the mission.
