Using Inherent Judicial Power in a State-Level Budget Dispute

State courts are in financial crisis. Since the mid-1990s, state legislatures have allowed funding for their judicial systems to stagnate or dwindle. With diminished resources, state courts have struggled to provide adequate access to justice and dispute resolution. The solution to this crisis may lie in the doctrine of inherent judicial power. Courts have historically used inherent power to request additional funds from local legislative bodies for discrete expenditures. The use of inherent power to challenge the overall sufficiency of a judicial budget, however, has proven troubling. Under the current formulation of the inherent-power doctrine, a state court contesting the adequacy of a statewide judicial budget runs into two problems. First, by invoking its inherent power to compel additional funding, the court may usurp the appropriation power of the legislature. Second, state courts threaten their own legitimacy by taking a portion of the state budget out of the political process. In response to these problems, this Note proposes a reformulation of the inherent-power doctrine. Specifically, state courts should invoke inherent power against a legislature only under a standard of absolute necessity to perform the duties required by federal and state constitutional law. This new standard limits the use of inherent power to situations that threaten the judiciary\u27s ability to perform its constitutionally mandated functions. By cabining the permitted uses of inherent power, the standard respects the separation of powers and preserves the judiciary\u27s public legitimacy

Final report for a brushless dc torque motor

Brushless direct current torque motor using permanent magnet rotor and three-phase winding in stationary armature for operation in vacuu

Brushless d.c. torque motor first quarterly report, 25 jun. - 25 sep. 1964

Brushless direct current torque moto

Evaluation of ignition mechanisms in selected spacecraft materials Final report, 1 Mar. - 30 Jun. 1967

Evaluation of ignition mechanisms for spacecraft materials in simulated spacecraft cabin atmosphere

An advanced brushless dc torque motor Quarterly report, 30 Sep. - 30 Dec. 1966

Design of torque motor controller, and operation of breadboard control circui

Offline Learning for Sequence-based Selection Hyper-heuristics

This thesis is concerned with finding solutions to discrete NP-hard problems. Such problems occur in a wide range of real-world applications, such as bin packing, industrial flow shop problems, determining Boolean satisfiability, the traveling salesman and vehicle routing problems, course timetabling, personnel scheduling, and the optimisation of water distribution networks. They are typically represented as optimisation problems where the goal is to find a ``best'' solution from a given space of feasible solutions. As no known polynomial-time algorithmic solution exists for NP-hard problems, they are usually solved by applying heuristic methods. Selection hyper-heuristics are algorithms that organise and combine a number of individual low level heuristics into a higher level framework with the objective of improving optimisation performance. Many selection hyper-heuristics employ learning algorithms in order to enhance optimisation performance by improving the selection of single heuristics, and this learning may be classified as either online or offline. This thesis presents a novel statistical framework for the offline learning of subsequences of low level heuristics in order to improve the optimisation performance of sequenced-based selection hyper-heuristics. A selection hyper-heuristic is used to optimise the HyFlex set of discrete benchmark problems. The resulting sequences of low level heuristic selections and objective function values are used to generate an offline learning database of heuristic selections. The sequences in the database are broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate between ``effective'' subsequences, that tend to lead to improvements in optimisation performance, and ``disruptive'' subsequences that tend to lead to worsening performance. Effective subsequences are used to improve hyper-heuristics performance directly, by embedding them in a simple hyper-heuristic design, and indirectly as the inputs to an appropriate hyper-heuristic learning algorithm. Furthermore, by comparing effective subsequences across different problem domains it is possible to investigate the potential for cross-domain learning. The results presented here demonstrates that the use of well chosen subsequences of heuristics can lead to small, but statistically significant, improvements in optimisation performance

The generation of a Gaussian random process in a position parameter

Analog computer method for approximating stationary Gaussian random process depending only on position paramete

The assessment of long-term orbital debris models

Existing long-term orbital debris models are assessed as a first step in the Air Force's effort to develop an Air Force long-term orbital debris model which can perform the following functions: (1) operate with the necessary accuracy at the relevant altitudes and orbital parameters; (2) benefit from new Air Force and non-Air Force debris measurements; and (3) accommodate current and future Air Force space scenarios. Model assessment results are shown for the NASA engineering model. The status of the NASA EVOLVE model assessment is discussed

Advisory Firm Employee Ownership and Performance in Separately Managed Accounts

I describe in detail the structure of separately managed accounts (SMAs) and how those accounts compare to and differ from mutual funds and hedge funds. I then examine how employee ownership of advisory firms — that is, firms in which employees have partnership or stock interests — affects the performance, idiosyncratic risk, and R-square of each firm’s SMA portfolios. In testing 14,484 different portfolios from more than 1,100 different advisory firms from 1995 to 2015, I find that SMAs at firms with employee ownership outperform SMAs at firms without it. The greatest impact is in the 25–50% employee-ownership range. Positive returns, risk, and all decrease as employee ownership increases beyond 50%, but SMA performance levels remain above those of firms in which the portfolio manager has no employee ownership. I also find that the Sharpe ratio is negatively related to employee ownership, reflecting a deterioration of risk-adjusted returns at higher employee-ownership levels. These results suggest both that the presence of advisor employee ownership is a significant, positive indicator for SMA performance and that those advisory firms assume more idiosyncratic risk to achieve these higher returns. For investors, my results show that employee ownership of advisory firms can be used as a differentiating factor to aid them in making SMA choices between portfolios with otherwise similar characteristics

Stochastic Orbit Prediction Using KAM Tori

Kolmogorov-Arnold-Moser (KAM) Theory states that a lightly perturbed, conservative, dynamical system will exhibit lasting quasi-periodic motion on an invariant torus. Its application to purely deterministic orbits has revealed exquisite accuracy limited only by machine precision. The theory is extended with new mathematical techniques for determining and predicting stochastic orbits for Earth satellite systems. The linearized equations of motion are developed and a least squares estimating environment is pioneered to fit observation data from the International Space Station to a phase space trajectory that exhibits drifting toroidal motion over a dense continuum of adjacent tori. The dynamics near the reference torus can be modeled with time-varying torus parameters that preserve both deterministic and stochastic effects. These parameters were shown to predict orbits for days into the future without tracking updates—a vast improvement over classical methods of orbit propagation that require routine updates