Promising State Policies for Personalized Learning

This report is a valuable resource for state policymakers—whether they are seeking to create conditions in state policy to support personalized learning, moving forward with initiatives to develop personalized learning pilot programs, hosting task forces to explore policy issues and needs, or taking a comprehensive policy approach for supporting advanced personalized learning models.Personalized learning is where instruction is tailored to each student's strengths, needs, and interests—including enabling student voice and choice in what, how, when, and where they learn—to provide flexibility and supports to ensure mastery of the highest standards possible

Environmental and biodiversity impacts of organic farming in the hills and uplands of Wales

1. Organic farming is based on principles of co-existence with natural systems, the minimisation of pollution and damage to the environment, and the promotion of the health of soil, plant and animal to produce healthy food with high standards of animal welfare and respect for the wider social and ecological impacts of the agricultural system. 2. Organic Farming has become an important aspect of EU agri-environment policy. Since the implementation of EC Reg. 2078/92 the EU promotes organic farming explicitly on its positive effects on the environment. 3. The environmental and biodiversity benefits of organic systems in the lowlands for mixed farming is generally accepted (Shepherd, 2003) but similar benefits for upland systems have not been identified. This report has been produced by OCW with funding from CCW to address this gap. Where relevant, means to ensure the beneficial impacts through changes to agri-environment schemes, organic standards, and education and dissemination are identified. 4. Hill and uplands are characterised as areas over 200m above sea level where the physical landscape results in production constraints. 5. Biodiversity losses linked to changes in hill and upland agriculture include the erosion of genetic diversity in farmed livestock and crops as well as in wildlife and flora, a reduction in habitat, soil and wildlife diversity and the loss of local knowledge and farming culture. 6. The organic approach to sustainable agriculture in hill or upland systems is through the use of multi species swards and mixed stocking. 7. The report identifies potential points of difference between organic and conventional management practices with regard to hill and upland farming and highlights research requirements to confirm or explore those potentials. 8. Conventional farms can adopt any or all of the practices of the organic farming system, but the engagement with the entire system and annual inspections are specific to the organic farmer. 9. The impacts are not just determined by the system of organic regulations and but also by the management ability and technical skills of the farmer and workers. 10. The practices on organic livestock farms identified that may differ from conventional and have direct biodiversity or environmental impacts are: lower stocking rates (overall manure loading maximum of 170kg/N/ha/yr); an adjustment of the stocking balance (increasing ratio of cattle to sheep); keeping indigenous breeds and strains adapted to the environmental conditions on the farm; limitation on products to control external parasites; reduction and restriction on the use of prophylactic veterinary medicines; the use of foragebased diets; storage and use of slurries, manures and composts, and constraints on the import and export of nutrients. 11. Organic practices in management of grassland and crops identified that may differ from conventional and have direct biodiversity or environmental impacts are: cessation of N fertiliser use; restriction on P & K use; use of lime to maintain pH; use of clovers and herbs in grass leys; cessation of use of chemical pesticides and all herbicides; mechanical and manual weed control and sensitive and timely cultivations; the use of mixed farm systems and rotations on in-bye land; the use of cover crops and undersowing; the use of green manures. 12. Organic regulations do not require habitat creation, but standards state, “that concern for the environment should manifest”…“in high standards of conservation management throughout the organic holding”. Discussion 13. Apart from practices that impact directly on biodiversity or the environment, each management decision on the farm will have knock-on effects that have their own consequences, for example welfare standards for livestock require bedding materials and greater housing space. 14. Organic farms operating solely in the hills and uplands can only be part of a system. Use of in-bye land or having a relationship with lowland holdings to provide winter-feed and forage is necessary to comply with regulations. This will increase the amount of lowland managed organically, bringing widely recognised environmental benefits. 15. Organic agriculture is, by legal definition, a system of production and is based on principles and uses practices adopted to optimise the health of the system. Any farmer may adopt individual practices, and the Tir Gofal scheme provides an opportunity for farmers to provide positive conservation measures, whether conventional or organic. Farming under the EU Regulation defining Organic farming provides assurance to the end consumer that the system used to produce or process the food product was according to that system. This provides a reliable means for consumers to support a system of agriculture that fits more closely with their expectations than intensive systems. 16. Any advantages of lower stocking rates and mixed stocking will only be maintained while organic farms are viable. Organic labelling provides an opportunity for consumers to make a positive choice for high welfare, environmentally benign systems; however the difficulties of marketing, the lack of consumer awareness of food production issues and unwillingness to pay are barriers to access to premium markets for many producers. Conclusions: 17. The potential benefits of individual practices outlined in the document are often clear, but there are currently few data to confirm the extent of some of the practices that may have most beneficial impact. The need for data on actual practices of the organic farmers in the hills and uplands is therefore highlighted. 18. Few Standards changes are recommended, however the monitoring of derogations to standards and use of restricted veterinary inputs is recommended. 19. Research and development needs, technical, education and dissemination, and agri-environment policy issues which may establish, ensure, or enhance the environmental and biodiversity impacts of organic farming in the hills and uplands are outlined. 20. Infrastructure work to integrate hill and upland and lowland systems is necessary to facilitate organic farming in the uplands; this may assist the viability of lowland organic holdings: the environmental benefits of which are established

Adaptive Control of Linear Modal Systems Using Residual Mode Filters and a Simple Disturbance Estimator

Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter

Resource Balancing Control Allocation

Next generation aircraft with a large number of actuators will require advanced control allocation methods to compute the actuator commands needed to follow desired trajectories while respecting system constraints. Previously, algorithms were proposed to minimize the l1 or l2 norms of the tracking error and of the control effort. The paper discusses the alternative choice of using the l1 norm for minimization of the tracking error and a normalized l(infinity) norm, or sup norm, for minimization of the control effort. The algorithm computes the norm of the actuator deflections scaled by the actuator limits. Minimization of the control effort then translates into the minimization of the maximum actuator deflection as a percentage of its range of motion. The paper shows how the problem can be solved effectively by converting it into a linear program and solving it using a simplex algorithm. Properties of the algorithm are investigated through examples. In particular, the min-max criterion results in a type of resource balancing, where the resources are the control surfaces and the algorithm balances these resources to achieve the desired command. A study of the sensitivity of the algorithms to the data is presented, which shows that the normalized l(infinity) algorithm has the lowest sensitivity, although high sensitivities are observed whenever the limits of performance are reached

Control Allocation with Load Balancing

Next generation aircraft with a large number of actuators will require advanced control allocation methods to compute the actuator commands needed to follow desired trajectories while respecting system constraints. Previously, algorithms were proposed to minimize the l1 or l2 norms of the tracking error and of the actuator deflections. The paper discusses the alternative choice of the l(infinity) norm, or sup norm. Minimization of the control effort translates into the minimization of the maximum actuator deflection (min-max optimization). The paper shows how the problem can be solved effectively by converting it into a linear program and solving it using a simplex algorithm. Properties of the algorithm are also investigated through examples. In particular, the min-max criterion results in a type of load balancing, where the load is th desired command and the algorithm balances this load among various actuators. The solution using the l(infinity) norm also results in better robustness to failures and to lower sensitivity to nonlinearities in illustrative examples

Adaptive Key Component Control of Nonlinear Evolving Flexible Structures

An optimal alarm system and its approximations may use Kalman filtering for univariate linear dynamic systems driven by Gaussian noise to provide a layer of predictive capability. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. An optimal alarm system can be designed to elicit the fewest false alarms for a fixed detection probability in this particular scenario

Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2

Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. This paper will be divided into two parts. Here in Part I we will review the basic adaptive control approach and introduce the primary ideas. In Part II, we will present the RMF methodology and complete the proofs of all our results. Also, we will apply the above theoretical results to a simple flexible structure example to illustrate the behavior with and without the residual mode filter

Adaptive Control Using Residual Mode Filters Applied to Wind Turbines

Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros

Direct Adaptive Control for Infinite-dimensional Symmetric Hyperbolic Systems

AbstractGiven a linear continuous-time infinite-dimensional plant on a Hilbert space and disturbances of known and unknown waveform, we show that there exists a stabilizing direct model reference adaptive control law with certain disturbance rejection and robustness properties. The closed loop system is shown to be exponentially convergent to a neighborhood with radius proportional to bounds on the size of the disturbance. The plant is described by a closed densely defined linear operator that generates a continuous semigroup of bounded operators on the Hilbert space of states.Symmetric Hyperbolic Systems of partial differential equations describe many physical phenomena such as wave behavior, electromagnetic fields, and quantum fields. To illustrate the utility of the adaptive control law, we apply the results to control of symmetric hyperbolic systems with coercive boundary conditions