An algorithm to median shortest path problem (MSPP) in the design of urban transportation networks

This paper proposes an efficient solution algorithm for realistic multi-objective median shortest path problems in the design of urban transportation networks. The proposed problem formulation and solution algorithm to median shortest path problem is based on three realistic objectives via route cost or investment cost, overall travel time of the entire network and total toll revenue. The proposed solution approach to the problem is based on the heuristic labeling and exhaustive search technique in criteria space and solution space of the algorithm respectively. The first labels each node in terms of route cost and deletes cyclic and infeasible paths in criteria space imposing cyclic break and route cost constraint respectively. The latter deletes dominated paths in terms of objectives vector in solution space in order to identify a set of Pareto optimal paths. The approach, thus, proposes a non-inferior solution set of Pareto optimal paths based on non-dominated objective vector and leaves the ultimate decision to decision-makers for purpose specific final decision during applications. A numerical experiment is conducted to test the proposed algorithm using artificial transportation network. Sensitivity analyses have shown that the proposed algorithm is advantageous and efficient over existing algorithms to find a set of Pareto optimal paths to median shortest paths problems.

Assessment of primary 5 students' mathematical modelling competencies

 Mathematical modelling is increasingly becoming part of an instructional approach deemed to develop students with competencies to function as 21st century learners and problem solvers. As mathematical modelling is a relatively new domain in the Singapore primary school mathematics curriculum, many teachers may not be aware of the learning outcomes and competencies needed to develop in their students during mathematical modelling. This paper reports on the assessment of two groups of Primary 5 students’ (aged 11) mathematical modelling competencies in their first attempt in completing a modelling task. The students’ competencies are assessed to be at levels 1 and 2 of a researcher-designed rubric. Findings appear to suggest that students faced particular challenges in formulating a mathematical problem from the real-world problem through making assumptions. Implications on teacher education on the facilitation of problem formulation and mathematisation during mathematical modelling at the primary level are drawn.

Short-term load and wind power forecasting using neural network-based prediction intervals

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Construction of neural network-based prediction intervals for short-term electrical load forecasting

Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study

The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones.

Communication of scientific uncertainty: International case studies on the development of folate and vitamin D Dietary Reference Values

Objective Transparent evidence-based decision making has been promoted worldwide to engender trust in science and policy making. Yet, little attention has been given to transparency implementation. The degree of transparency (focused on how uncertain evidence was handled) during the development of folate and vitamin D Dietary Reference Values was explored in three a priori defined areas: (i) value request; (ii) evidence evaluation; and (iii) final values. Design Qualitative case studies (semi-structured interviews and desk research). A common protocol was used for data collection, interview thematic analysis and reporting. Results were coordinated via cross-case synthesis. Setting Australia and New Zealand, Netherlands, Nordic countries, Poland, Spain and UK. Subjects Twenty-one interviews were conducted in six case studies. Results Transparency of process was not universally observed across countries or areas of the recommendation setting process. Transparency practices were most commonly seen surrounding the request to develop reference values (e.g. access to risk manager/assessor problem formulation discussions) and evidence evaluation (e.g. disclosure of risk assessor data sourcing/evaluation protocols). Fewer transparency practices were observed to assist with handling uncertainty in the evidence base during the development of quantitative reference values. Conclusions Implementation of transparency policies may be limited by a lack of dedicated resources and best practice procedures, particularly to assist with the latter stages of reference value development. Challenges remain regarding the best practice for transparently communicating the influence of uncertain evidence on the final reference values. Resolving this issue may assist the evolution of nutrition risk assessment and better inform the recommendation setting process.

Polyhedral results and exact algorithms for the asymmetric travelling salesman problem with replenishment arcs

The asymmetric travelling salesman problem with replenishment arcs (RATSP), arising from work related to aircraft routing, is a generalisation of the well-known ATSP. In this paper, we introduce a polynomial size mixed-integer linear programming (MILP) formulation for the RATSP, and improve an existing exponential size ILP formulation of Zhu [The aircraft rotation problem, Ph.D. Thesis, Georgia Institute of Technology, Atlanta, 1994] by proposing two classes of stronger cuts. We present results that under certain conditions, these two classes of stronger cuts are facet-defining for the RATS polytope, and that ATSP facets can be lifted, to give RATSP facets. We implement our polyhedral findings and develop a Lagrangean relaxation (LR)-based branch-and-bound (BNB) algorithm for the RATSP, and compare this method with solving the polynomial size formulation using ILOG Cplex 9.0, using both randomly generated problems and aircraft routing problems. Finally we compare our methods with the existing method of Boland et al. [The asymmetric traveling salesman problem with replenishment arcs, European J. Oper. Res. 123 (2000) 408–427]. It turns out that both of our methods are much faster than that of Boland et al. [The asymmetric traveling salesman problem with replenishment arcs, European J. Oper. Res. 123 (2000) 408–427], and that the LR-based BNB method is more efficient for problems that resemble the aircraft rotation problems.

Formulation optimization for high drug loading colonic drug delivery carrier

High drug loading (DL) carrier is an effective way to cure the cancerous cells. High drug loading is also one of the key issues in the drug delivery research, especially the colonic drug delivery system by oral administration. The times of drug intake could be remarkably reduced if high drug loading carriers are administered. At the same time, the related formulation materials could be effectively utilized. One major obstacle with the preparation of this system is the difficulty to encapsulate the hydrophilic drug into hydrophobic encapsulation polymer. A design of high drug loading delivery system with biodegradable, biocompatible materials and optimization of the fabrication process is a potential solution to solve the problem. So in this research, 5-Fluorouracil (5-FU) loaded Poly (lactide-co-glycolide) (PLGA) nanoparticles were prepared by double emulsion and solvent evaporation method. Several fabrication parameters including theoretical drug loading, volume ratio of outer water phase to the first emulsion, pH value of outer aqueous phase and emulsifier PVA concentration were optimized to get a high drug loading nanoparticles. The result shows that with the increase of theoretical drug loading, the actual drug loading increased gradually. When adjusted the pH value of outer aqueous phase to the isoelectric point (8.02) of 5-Fluorouracil, the drug loading exhibited a higher one compared to other pH value solution. Relative higher volume ratio of outer water phase to the first emulsion was also beneficial for the enhancement of drug loading. But the nanoparticles size increased simultaneously due to the lower shearing force. When increased the PVA concentration, the drug loading showed an increase first and following a drop.

Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search

This paper investigates a new approach for solving the multiobjective job shop scheduling problem, namely the Cuckoo Search ( CS) approach. The requirement is to schedule jobs on a single machine so that the total material waste is minimised as well as the total tardiness time. The material waste is quantified in terms of saving factors to show the reduction in material that can be achieved when producing two jobs with the same materials in sequence. The estimated saving factor is used to calculate a cost savings for each job based on its material type. A formulation of multiobjective optimisation problems is adopted to generate the set of schedules that maximise the overall cost savings and minimise the total tardiness time. where all trade-offs are considered for the two conflicting objectives. A Pareto Archived Multiobjective Cuckoo Search (PAMOCS) is developed to find the set ofnondominated Pareto optimal solutions. The solution accuracy of PAMOCS is shown by comparing the closeness of the obtained solutions to the true Pareto front generated by the complete enumeration methad. Results shaw that CS is a very effective and promising technique to solve job shop scheduling problems.

PhD crisis discourse: a critical approach to the framing of the problem and some Australian ‘solutions’

A feature of HE reform discourse is the tendency to construct the rationale for reform in terms of averting calamity and risk. We refer to this risk talk as ‘crisis discourse’. This study examines the formulation of PhD crisis discourse internationally and in Australia. We find that a key feature of PhD crisis discourse is that universities are producing too many graduates for too few academic jobs; and graduates lack skills that enable them to be productive in jobs outside academia. In Australia, the discourse has shifted from one dominated by efficiency concerns from the late 1990s to the present focus on graduate skills and employability. The policy solution to the efficiency crisis in the Australian PhD resulted in system-wide changes in research training funding focused on increased efficiency. The current unemployability discourse has as yet prompted isolated institutional responses, the introduction of new PhD programs or re-badging existing offerings as pro-skills development offerings. Following an examination of three Australian institutional responses, we conclude that the crisis discourse signals tensions surrounding the PhD: should achievement in doctoral education be measured by outcomes in intellectual excellence or the responsiveness of qualification to the current needs and priorities of society?

PhD crisis discourse : a critical approach to the framing of the problem and some Australian ‘solutions’

 A feature of HE reform discourse is the tendency to construct the rationale for reform in terms of averting calamity and risk. We refer to this risk talk as 'crisis discourse'. This study examines the formulation of PhD crisis discourse internationally and in Australia. We find that a key feature of PhD crisis discourse is that universities are producing too many graduates for too few academic jobs; and graduates lack skills that enable them to be productive in jobs outside academia. In Australia, the discourse has shifted from one dominated by efficiency concerns from the late 1990s to the present focus on graduate skills and employability. The policy solution to the efficiency crisis in the Australian PhD resulted in system-wide changes in research training funding focused on increased efficiency. The current unemployability discourse has as yet prompted isolated institutional responses, the introduction of new PhD programs or re-badging existing offerings as pro-skills development offerings. Following an examination of three Australian institutional responses, we conclude that the crisis discourse signals tensions surrounding the PhD: should achievement in doctoral education be measured by outcomes in intellectual excellence or the responsiveness of qualification to the current needs and priorities of society? © 2014 Springer Science+Business Media Dordrecht.

On the multicast lifetime of WANETs with multibeam antennas : Formulation, algorithms, and analysis

We explore the multicast lifetime capacity of energy-limited wireless ad hoc networks using directional multibeam antennas by formulating and solving the corresponding optimization problem. In such networks, each node is equipped with a practical smart antenna array that can be configured to support multiple beams with adjustable orientation and beamwidth. The special case of this optimization problem in networks with single beams have been extensively studied and shown to be NP-hard. In this paper, we provide a globally optimal solution to this problem by developing a general MILP formulation that can apply to various configurable antenna models, many of which are not supported by the existing formulations. In order to study the multicast lifetime capacity of large-scale networks, we also propose an efficient heuristic algorithm with guaranteed theoretical performance. In particular, we provide a sufficient condition to determine if its performance reaches optimum based on the analysis of its approximation ratio. These results are validated by experiments as well. The multicast lifetime capacity is then quantitatively studied by evaluating the proposed exact and heuristic algorithms using simulations. The experimental results also show that using two-beam antennas can exploit most lifetime capacity of the networks for multicast communications. © 2013 IEEE.

In vitro and in vivo assessment of docetaxel formulation developed for esophageal stents

Esophageal cancer (EC) mostly affects the elderly population and is frequently diagnosed at an advanced stage. Self-expanding metal stents (SEMS) are the most popular mode of palliation, but they are associated with reocclusion caused by tumor growth. To overcome this problem, docetaxel (DTX)-loaded polyurethane formulations were prepared for stent application. The films were evaluated against the cancer cell lines, OE-19 and OE-21, and normal esophageal cell line Het-1A. The DTX and the formulations were evaluated in vitro for the cytotoxicity and in vivo in nude mice. It was found that DTX and the formulations have a weak activity against the EC cell lines and an even weaker activity against Het-1A cell line. Preliminary in vivo studies showed skin toxicity in nude mice necessitating modification of the formulation. Reevaluation in a mouse xenograft model resulted in toxicity at high dose formulations while the low dose formulation exhibited modest advantage over commercial IV formulation; however, there was no significant difference between the commercial IV and blank formulation. DTX combination with an anti-cancer agent having complementary mode of action and non-overlapping toxicity could yield better outcome in future.