Rc Glider Plans 14.epub: Explore the History and Culture of Brazilian Gliders with Extraviador Senio

Sokol SA, Agnew DW, Lewis AD, Southard TL, Miller ADPulmonary hyalinosis in captive sugar gliders ( Petaurus breviceps).J Vet Diagn Invest. 2017 Sep;29(5):691-695. doi: 10.1177/1040638717703683. Epub2017 Jun 28.

Abstract:The paper presents a power electronic conversion system and its control for a fuel cell and a battery-based hybrid drive system for a motor glider. The energy conversion system is designed in such a way that the fuel cell gives power equal to the electric drive power demand for horizontal flight, whereas during motor glider take-off and climbing, the fuel cell is supported by the battery. The paper presents the power demand related to the assumed mission profile, the main components of the hybrid drive system and its holistic structure, and details of power electronics control. Selected stationary experimental test results related to the energy conversion and drive system are shown. Some results related to the aircraft tests on a runway are presented.Keywords: fuel cell; energy conversion; energy management; power converter; aircraft propulsion

Rc Glider Plans 14.epub

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Earlier studies indicate that moving through urban landscape can be impeded by the urbanization of the matrix since sugar gliders (Petaurus breviceps) move less in the more urbanized matrix [22] and squirrel gliders (Petaurus norfolcensis) are using larger core areas in continuous forests than in forest fragments [70]. Results of flying squirrel males show that nightly movement distances were longer in home ranges that contained a lot of suitable forests than in home ranges that contained a lot of urban habitat types. Male flying squirrels have large territories, can traverse throughout their home range within one night, and regularly visit territories of several females [36]. However, when a home range involves a large fraction of inhospitable habitat, individual may spend several days in one part of the home range before crossing through the matrix to reach another core area due to costs of moving through matrix. In contrast, the availability of suitable habitat had less influence on the nightly moved distances of female flying squirrels, whose home ranges are smaller and often confined within one suitable forest patch [36]. In our case, home ranges of females included more mature spruce-dominated forests that are suitable for breeding than home ranges of males (44 % vs. 24 %). Thus, females have most probably chosen forest patches that are big enough for breeding and raising the young, and as they are territorial they virtually never move outside the home-range they are occupying.

Figure 2. Weight changes over the Euclidean distance at different values of a. When a = 0.2 is tuned for 24-h forecast, as Wf(i) will approach a = 0 at 24 km (equivalent to the 24-h traveling distance of a glider). Likewise, a = 0.1 is tuned for a 72-h forecast.

Figure 7. Results of OceanGNS tests for the Gulf of St. Lawrence deployment. (A) Shows the glider speed (dots) toward its intended waypoint (destination) for every dive segment calculated from GPS positions. The green shaded period is the time when OceanGNS was used. The blue line is the calculated ideal speed the glider could achieve based on the constant value of 0.27 m/s and the dead reckoned currents estimated by the glider (Equation 7). (B) Histogram of ideal vs. achieved glider speed toward the target. The residuals of the ideal speed vs. the actual speed toward the waypoint shows that during the OceanGNS test period, the glider speed exceeded the ideal value by an average of 27%.

Figure 8. Results of OceanGNS tests for the Labrador Sea mission. (A) Shows the glider speed (dots) toward its intended waypoint (destination) for every dive segment calculated from GPS positions. The shaded period is the time when OceanGNS was used. The blue line is the calculated ideal speed the glider could achieve based on the constant value of 0.27 m/s and the dead reckoned currents estimated by the glider (Equation 7). (B) Histogram of ideal vs. achieved glider speed toward the target. The residuals of the ideal speed vs. the actual speed toward the waypoint shows that during the OceanGNS test period, the glider speed exceeded the ideal value by an average of almost 10%.

FIGURE 3. Survey domain near Cape Hatteras. The curve represents glider trajectory during the first PEACH deployment. The red line path is the pre-assigned sampling pattern. Squares denote the glider surfacing positions along trajectory, and color of the trajectory depicts timestamps. The arrows represent the NCOM-predicted flow field at the starting time of the deployment.

Police patrols play an important role in public safety. The patrol district design is an important factor affecting the patrol performances, such as average response time and workload variation. The redistricting or redrawing police command boundaries can be described as partitioning a police jurisdiction into command districts with the constraints such as contiguity and compactness. The size of the possible sample space is large and the corresponding graph-partitioning problem is NP-complete. In our approach, the patrol districting plans generated by a parameterized redistricting procedure are evaluated using an agent-based simulation model we implemented in Java Repast in a geographic information system (GIS) environment. The relationship between districting parameters and response variables is studied and better districting plans can be generated. After in-depth evaluations of these plans, we perform a Pareto analysis of the outputs from the simulation to find the non-dominated set of plans on each of the objectives. This paper also includes a case study for the police department of Charlottesville, VA, USA. Simulation results show that patrol performance can be improved compared with the current districting solution.

The generation of districting plans is based on atomic geographical units. There are some existing geographical units such as police beats or census blocks. Usually, these geographical units consider administrative boundaries, important roads, or some natural boundaries (mountains, rivers). The redistricting procedure can start from these units and re-group them into several districts. When developing districting plans for large areas containing hundreds of such geographical units, police beats or census blocks are good choices for atomic units. However, some cities only have 20 or 30 census blocks. Police beats or census blocks are not small enough for optimal patrol boundaries. In such case, grid network can be used instead. The city can be divided into several hundreds of grids and they are small enough to be atomic units. Clearly, more atomic units represent more possible districting plans. Such representation is more suitable for systematic and scientific study of districting problem. The output districting plans based on grid boundaries can be adjusted according to existing boundaries such as important roads, administrative boundaries and natural boundaries.

The locations of the seeds for districts determine the framework and basic structure of the districting plans. The relevant parameters for starting the growing process are: 1) the center of the concentric circles, 2) the number of circles, 3) the radius of each circle, 4) the number of seeds on each circle. Additional parameters determine the course of district growth. Examples are: 5) the stopping criterion, 6) the number of growth iterations, 7) growth randomness vs. compactness, 8) the number of iterations that balance the CFS probability between districts, 9) the number of iterations that smooth the boundary between districts. Therefore, a districting plan can be described and represented by a set of districting parameters. Once a districting plan is generated, some measurements can be quickly calculated without detailed simulation evaluation, such as compactness of plans and the variation of CFS probability of all districts. These intermediate measurements of districting plans can be used to select top proportion of plans for further simulation evaluation.

Without prior knowledge about how districting parameters affect final response variables, we randomize these parameters to generate some plans, quickly calculate the intermediate measurements, and take some time to get final responses through simulation evaluation. Then we build statistical models to study the relation between them, especially how districting parameters and intermediate measurements affect the final performance variables. The districting parameters can be adjusted to generate more plans that may have better performances. Since it is time intensive to use simulation to evaluate these plans, they can be ranked by the combined weighted score of the intermediate metrics. The weights are adjusted based on the relation between intermediate variables and final performance variables.

Because the assessment of response times and workloads requires the incorporation of multiple factors that interact in complex ways we cannot use closed form expressions. Also, field experiments in the law enforcement and safety management are clearly not feasible because of the risks and costs, not to mention, the public relations problems [14]. This means that evaluation of the police patrol districting plans requires a high fidelity simulation. A feature key needed in this simulation is the ability to accurately represent behaviors of the police in response to calls-for-service. Agent-based simulations afford the ability to effectively represent these behaviors.

Due to the NP-completeness of the graph-partition problem, there are too many possibilities of districting plans. We cannot use exact method to evaluate each of them. The evaluated districting plans in this case study are only a small proportion of the whole solution set and the solutions provided are preliminary. The global optimality cannot be guaranteed. We only find some significant districting parameters and intermediate measurements that may lead to better plans. More rigorous experimental design and statistical analysis can be conducted to further study the relationship between these factors and responses. With more powerful computational resources, more districting plans can be further generated and evaluated. It is possible to make improvements on both response variables. 2ff7e9595c