Cross cultural exchange to support reasoning about socio-scientific sustainability issues

In this article, we describe a project on reasoning about socio-scientific issues (SSIs), involving French and Australian pre-service science teachers engaged in on-line discussion and development of a wiki. In the research, we developed frameworks for looking at the quality of reasoning about 'socially acute' sustainability questions. We found the level of reasoning was enhanced by the cross-cultural exchange, and identified the importance of context in framing reasoning quality. We argue that science teachers could effectively adapt this approach to develop students' scientific literacy and embed the 'science as a human endeavour' strand of the Australian Curriculum in their practice.

Australian Mothers' notions of risk and uncertainty in relation to their pre-teen children

In this article we examine the ways discourses of risk manifested and played out within and across two groups of Australian mothers living in two large urban centres in Australia: the first comprised of mothers who had a pre-teen child diagnosed with an eating disorder (n = 13); the second of mothers who had a pre-teen child without the symptoms or diagnosis of an eating disorder (n = 13). In 2011 and 2012, we conducted in-depth interviews with the mothers in their homes on their ideas about health and their relationships with their children. An analysis of the data collected from these interviews indicated that having a pre-teen child diagnosed with an eating disorder had a decisive impact on how the mothers constituted and responded to risk. For mothers, who had a pre-teen child with an eating disorder, risk was intensified by bio-medical discourses. The particular intensifications of risk limited the ways in which mothers could act and often threatened to undermine their abilities as competent carers. By contrast, the mothers who did not have a pre-teen child with an eating disorder spoke about risk less directly, and with less sense of immediacy. Where these mothers acknowledged risk discourses particularly in regard to health, they were in a stronger position to negotiate them. Our analysis indicates that the ways in which mothers responded to risk is contingent on circumstances and contexts. Mothers’ responses to risk were related to the calculability of the risk and their perceived capacity to manage it.

Embedding systemic science with FFSR and DPI, phase 2 : final project report

Systemic Science (systems thinking) provides a structured approach to better conceptualise and operationalise problems and to deal with complexity, uncertainty and risk. Systemic Science is different from traditional forms of scientific investigation because it focuses on 'the whole' rather than the component parts.

Systemic science is a skill that DPI generally lacks, although certain sections of the organisation do employ its principles with success. The objective of this project was to enhance DPI's existing systems thinking capability, with particular emphasis on FFSR, to develop the skills in more staff across the organisation, and to showcase the merits of the approach by applying the concepts on existing/planned projects. This report details ‘Phase 2’ (year 2) of the project and as such, outputs from the separate ‘Phase 1’ are not reported here.

International sex offender registration laws : research and evaluation issues based on a review of current scientific literature

In many jurisdictions, anyone convicted of a sexual offense is required to register with police, often for life. Nine different countries have now implemented sex offender registries in an attempt to protect the public from the perceived threat posed by sexual offenders. Yet such laws have been criticized as being overly inclusive, tying up limited law enforcement resources to track many offenders who pose little risk of sexual reoffending. This paper considers the available research evidence relevant to the effectiveness of such laws for the deterrence of sexual offending and the investigation of sex crimes. It is concluded that significant gaps persist in our knowledge of whether existing laws effectively reduce sexual offending or reoffending and that large-scale, well-designed studies of the impact of sex offender registration on rates of offending, the collateral consequences to offenders and their families, and the costs of such laws are needed.

Developing an analytical framework to assess the uncertainty and flexibility mismatches across the supply chain

Purpose: The purpose of this paper is to discuss how decisions regarding organisational flexibility can be improved through targeted resource allocation, by focusing on the supply chain's level of uncertainty exposure. Specifically, the issue of where and in what ways flexibility has been incorporated across the organisation's supply chain is addressed. Design/methodology/approach: A two-phase methodology design based on literature review and case study was used. Using 83 journal articles in the areas of uncertainty and flexibility an analytical process for assessing uncertainty-flexibility mismatches was developed. Furthermore, results from ten interviews with senior/middle managers within the Australian manufacturing sector were used to provide preliminary insights on the usefulness and importance of the analytical process and its relationship with organisational practice. Findings: The paper emphasises the importance of having a systematic and encompassing view of uncertainty-flexibility mismatches across the supply chain, as well as the significance of socio-technical engagement. The paper both conceptually and empirically illustrates how, using a structured analytical process, flexibility requirements across the supply, process, control and demand segments of a supply chain might be assessed. A four-step analytical process was accordingly developed and, its application, usefulness and importance discussed using empirical data. Practical implications: The analytical process presented in this paper can assist managers to obtain a comprehensive overview of supply chain flexibility when dealing with situations involving uncertainty. This can facilitate and improve their decision-making with respect to prioritising attention on identified flexibility gaps in order to ensure stability of their performance. Originality/value: The paper presents a supply chain-wide discussion on the difficulties that uncertainty brings to organisations, and how organisational flexibility might serve to moderate those challenges for supply chain management. It discusses how to identify the flexibility gap and proposes an original analytical process for systematic assessment of uncertainty-flexibility mismatches. © Emerald Group Publishing Limited.

Effectiveness of interventions for convicted DUI offenders in reducing recidivism: a systematic review of the peer-reviewed scientific literature

Abstract Background: Driving under the influence (DUI) is a major cause of death and disability. Although a broad array of programs designed to curb DUI incidents are currently offered to both first-time and recidivist DUI offenders, existing evaluations of the effectiveness of these programs have reported mixed results. Objective: To synthesize the results of DUI program evaluations and determine the strength of the available evidence for reducing recidivism for different types of programs. Methods: A systematic review of all EBSCO databases, EMBASE, PubMed, ProQuest, Sociological Abstracts and TRIS was conducted to identify evaluations of treatments/interventions to prevent DUI offenses. Additional articles were identified from reference lists of relevant articles. Results: A total of 42 relevant studies were identified by the search strategy. Of these, 33 utilized non-experimental evaluation designs or reported insufficient data to allow effect sizes to be calculated, making meta-analysis unfeasible. Evaluations of several different program types reported evidence of some level of effectiveness. Conclusion: Because of the general lack of high quality evidence assessing the effectiveness of DUI prevention programs, it is not possible to make conclusive statements about the types of programs that are likely to be most effective. Nonetheless, there was some evidence to support the effectiveness of programs that utilize intensive supervision and education. There is a need for future evaluations to adopt more scientifically rigorous research designs to establish the effects of these programs.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

Quantification of spatial and thematic uncertainty in the application of underwater video for benthic habitat mapping

This study presents an analysis of the application of underwater video data collected for training and validating benthic habitat distribution models. Specifically, we quantify the two major sources of error pertaining to collection of this type of reference data. A theoretical spatial error budget is developed for a positioning system used to co-register video frames to their corresponding locations at the seafloor. Second, we compare interpretation variability among trained operators assessing the same video frames between times over three hierarchical levels of a benthic classification scheme. Propagated error in the positioning system described was found to be highly correlated with depth of operation and varies from 1.5m near the surface to 5.7m in 100m of water. In order of decreasing classification hierarchy, mean overall observer agreement was found to be 98% (range 6%), 82% (range 12%) and 75% (range 17%) for the 2, 4, and 6 class levels of the scheme, respectively. Patterns in between-observer variation are related to the level of detail imposed by each hierarchical level of the classification scheme, the feature of interest, and to the amount of observer experience. © 2014 Copyright © Taylor & Francis Group, LLC.

Extending scheduling algorithms to minimise the impact of bounded uncertainty using interval programming

 This research proposed a new methodology to extend algorithms to accept interval-based uncertain parameters. The methodology is applied on scheduling algorithms, including heuristic and meta-heuristic algorithms and produced optimal results with higher accuracy. The research outcomes are effective for decision making process using uncertain or predicted data.

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.

Scientific and Religious Mindsets: A Candid Look at the Perils of a Panglossian President

Through Voltaire’s novella Candide, this essay examines the differences between a scientific and a religious mindset and the consequences of poor decision-making when a political leader has a religious mindset that he misapplies to fundamentally scientific questions. By analyzing various decisions that President Bush has made, it is argued that he has a religious mindset that has resulted in several fallacious choices of extreme import, yielding considerable losses. As such, a decision-maker with power should be able to distinguish questions best answered with a scientific mindset from those that are in the realm of philosophy or religion and apply a scientific mindset to the former. A scientific mindset formulates a theory that yields testable propositions, it acquires data and uses that to evaluate the verity of the theory. As the data contradict the theory’s predictions, the theory is adjusted. The religious mindset proffers certain explanations but then holds steadfastly to them. It ignores contrary evidence, does not adjust its tenets, or alter its prescribed behaviors, attacks the integrity of those who espouse contrarian viewpoints, and commits logical falla- cies, such as inverting the causative relationship.