Optimal fluid injection strategies for in situ mineral leaching in two-dimensions

This paper examines the ground-water flow problem associated with the injection and recovery of certain corrosive fluids into mineral bearing rock. The aim is to dissolve the minerals in situ, and then recover them in solution. In general, it is not possible to recover all the injected fluid, which is of concern economically and environmentally. However, a new strategy is proposed here, that allows all the leaching fluid to be recovered. A mathematical model of the situation is solved approximately using an asymptotic solution, and exactly using a boundary integral approach. Solutions are shown for two-dimensional flow, which is of some practical interest as it is achievable in old mine tunnels, for example.

Towards a relational contracting framework in the Australian construction industry : an initial framework

The Australian construction industry is characterized as being a competitive and risky business environment due to lack of cooperation, insufficient trust, ineffective communication and adversarial relationships which are likely lead to poor project performance. Relational contracting (RC) is advocated by literature as an innovative approach to improve the procurement process in the construction industry. Various studies have collectively added to the current knowledge of known RC norms, but there seem to be little effort on investigating the determinants of RC and its impact on project outcomes. In such circumstances, there is lack of evidence and explanation on the manner on how these issues lead to different performance. Simultaneously, the New Engineering Contract (NEC) that embraced the concept of RC is seen as a modern way of contracting and also considered as one of the best approaches to the perennial problem of improving adversarial relationships within the industry. The reality of practice of RC in Australia is investigated through the lens of the NEC. A synthesis of literature views on the concept, processes and tools of RC is first conducted to develop the framework of RC. A case study approach is proposed for an in-depth analysis to explore the critical issues addressed by RC in relation to project performance. Understanding the realities of RC will assist stakeholders in the construction industry with their investment in RC.

Optimal voltage support mechanism in distribution networks

Optimal scheduling of voltage regulators (VRs), fixed and switched capacitors and voltage on customer side of transformer (VCT) along with the optimal allocaton of VRs and capacitors are performed using a hybrid optimisation method based on discrete particle swarm optimisation and genetic algorithm. Direct optimisation of the tap position is not appropriate since in general the high voltage (HV) side voltage is not known. Therefore, the tap setting can be determined give the optimal VCT once the HV side voltage is known. The objective function is composed of the distribution line loss cost, the peak power loss cost and capacitors' and VRs' capital, operation and maintenance costs. The constraints are limits on bus voltage and feeder current along with VR taps. The bus voltage should be maintained within the standard level and the feeder current should not exceed the feeder-rated current. The taps are to adjust the output voltage of VRs between 90 and 110% of their input voltages. For validation of the proposed method, the 18-bus IEEE system is used. The results are compared with prior publications to illustrate the benefit of the employed technique. The results also show that the lowest cost planning for voltage profile will be achieved if a combination of capacitors, VRs and VCTs is considered.

Relational contracting and its efficacy of project performance in the Australian construction industry

The Australian construction industry is characterized as being a competitive and risky business environment due to lack of cooperation, insufficient trust, ineffective communication and adversarial relationships which are likely lead to poor project performance. Relational contracting (RC) is advocated by literature as an innovative approach to improve the procurement process in the construction industry. Various studies have collectively added to the current knowledge of known RC norms, but there seem to be little effort on investigating the determinants of RC and its efficacy on project outcomes. In such circumstances, there is lack of evidence and explanation on the manner on how these issues lead to different performance. Simultaneously, the New Engineering Contract (NEC) that embraced the concept of RC is seen as a modern way of contracting and also considered as one of the best approaches to the perennial problem of improving adversarial relationships within the industry. The reality of practice of RC in Australia is investigated through the lens of the NEC. A synthesis of literature views on the concept, processes and tools of RC is first conducted to develop the framework of RC. A case study approach is proposed for an in-depth analysis to explore the critical issues addressed by RC in relation to project performance. Understanding the realities of RC will assist stakeholders in the construction industry with their investment in RC.

A Stochastic View of Optimal Regret through Minimax Duality

We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's action sequence, of the difference between a sum of minimal expected losses and the minimal empirical loss. We show that the optimal regret has a natural geometric interpretation, since it can be viewed as the gap in Jensen's inequality for a concave functional--the minimizer over the player's actions of expected loss--defined on a set of probability distributions. We use this expression to obtain upper and lower bounds on the regret of an optimal strategy for a variety of online learning problems. Our method provides upper bounds without the need to construct a learning algorithm; the lower bounds provide explicit optimal strategies for the adversary. Peter L. Bartlett, Alexander Rakhlin

Acquire a practical overview of 'good faith' in commercial contracting

The twists and turns in the ongoing development of the implied common law good faith obligation in the commercial contractual arena continue to prove fertile academic ground. Despite a lack of guidance from the High Court, the lower courts have been besieged by claims based, in part, on the implied obligation. Although lower court authority is lacking consistency and the ‘decisions in which lower courts have recognised the legitimacy of implication of a term of good faith vary in their suggested rationales’, the implied obligation may provide some comfort to a party to ‘at least some commercial contracts’ faced with a contractual counterpart exhibiting symptoms of bad faith.

Optimal control of distributed generators and capacitors by hybrid DPSO

In this paper, a comprehensive planning methodology is proposed that can minimize the line loss, maximize the reliability and improve the voltage profile in a distribution network. The injected active and reactive power of Distributed Generators (DG) and the installed capacitor sizes at different buses and for different load levels are optimally controlled. The tap setting of HV/MV transformer along with the line and transformer upgrading is also included in the objective function. A hybrid optimization method, called Hybrid Discrete Particle Swarm Optimization (HDPSO), is introduced to solve this nonlinear and discrete optimization problem. The proposed HDPSO approach is a developed version of DPSO in which the diversity of the optimizing variables is increased using the genetic algorithm operators to avoid trapping in local minima. The objective function is composed of the investment cost of DGs, capacitors, distribution lines and HV/MV transformer, the line loss, and the reliability. All of these elements are converted into genuine dollars. Given this, a single-objective optimization method is sufficient. The bus voltage and the line current as constraints are satisfied during the optimization procedure. The IEEE 18-bus test system is modified and employed to evaluate the proposed algorithm. The results illustrate the unavoidable need for optimal control on the DG active and reactive power and capacitors in distribution networks.

Relational contracting and its efficacy of project outcomes in the Australian construction industry

The Australian construction industry is characterized as being a competitive and risky business environment due to lack of cooperation, insufficient trust, ineffective communication and adversarial relationships which are likely lead to poor project performance. Relational contracting (RC) is advocated by literature as an innovative approach to improve the procurement process in the construction industry. Various studies have collectively added to the current knowledge of known RC norms, but there seem to be little effort on investigating the determinants of RC and its efficacy on project outcomes. In such circumstances, there is a lack of evidence and explanation on the manner on how these issues lead to different performance. Simultaneously, the New Engineering Contract (NEC) that embraced the concept of RC is seen as a modern way of contracting and also considered as one of the best approaches to the perennial problem of improving adversarial relationships within the industry. The reality of practice of RC in Australia is investigated through the lens of the NEC. A synthesis of literature views on the concept, processes and tools of RC is first conducted to develop the framework of RC.

Optimal-stopping control for airborne collision avoidance and return-to-course flight

This paper considers an aircraft collision avoidance design problem that also incorporates design of the aircraft’s return-to-course flight. This control design problem is formulated as a non-linear optimal-stopping control problem; a formulation that does not require a prior knowledge of time taken to perform the avoidance and return-to-course manoeuvre. A dynamic programming solution to the avoidance and return-to-course problem is presented, before a Markov chain numerical approximation technique is described. Simulation results are presented that illustrate the proposed collision avoidance and return-to-course flight approach.

Optimal online prediction in adversarial environments

In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

Optimal online prediction in adversarial environments

In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

Optimal strategies and minimax lower bounds for online convex games

A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.