Proposed MBMs for reduction of greenhouse gas emissions from international shipping and the WTO rules

The international shipping sector is a major contributor to global greenhouse gas (GHG) emissions. The International Maritime Organisation (IMO) has adopted some technical and operational measures to reduce GHG emissions from international shipping. However, these measures may not be enough to reduce the amount of GHG emissions from international shipping to an acceptable level. Therefore, the IMO Member States are currently considering a number of proposals for the introduction of market-based measures (MBMs). During the negotiation process, some leading developing countries raised questions about the probable confl ict of the proposed MBMs with the rules of the World Trade Organisation (WTO). This article comprehensively examines this issue and argues that none of the MBM proposals currently under consideration by the IMO has any confl ict with the WTO rules.

Digital Modeling, Integrated Project Delivery and Industry Transformation: An Australian Case Study

This research is focused on realizing productivity benefits for the delivery of transport infrastructure in the Australian construction industry through the use of building information modeling (BIM), virtual design and construction (VDC) and integrated project delivery (IPD). Specific objectives include: (I) building an understanding of the institutional environment, business systems and support mechanisms (e.g., training and skilling) which impact on the uptake of BIM/VDC; (II) gathering data to undertake a cross-country analysis of these environments; and (III) providing strategic and practical outcomes to guide the uptake of such processes in Australia. Activities which will inform this research include a review of academic literature and industry documentation, semi-formal interviews in Australia and Sweden, and a cross-country comparative analysis to determine factors affecting uptake and associated productivity improvements. These activities will seek to highlight the gaps between current-practice and best-practice which are impacting on widespread adoption of BIM/VDC and IPD. Early findings will be discussed with intended outcomes of this research being used to: inform a national public procurement strategy; provide guidelines for new contractual frameworks; and contribute to closing skill gaps. Keywords: building information modeling (BIM); virtual design and construction (VDC); integrated project delivery (IPD); transport infrastructure; Australia; procurement

Greenhouse gas emissions from sub-tropical agricultural soils after addition of organic by-products

As the cost of mineral fertilisers increases globally, organic soil amendments (OAs) from agricultural sources are increasingly being used as substitutes for nitrogen. However, the impact of OAs on the production of greenhouse gases (CO2 and N2O) is not well understood. A 60-day laboratory incubation experiment was conducted to investigate the impacts of applying OAs (equivalent to 296 kg N ha−1 on average) on N2O and CO2 emissions and soil properties of clay and sandy loam soils from sugar cane production. The experiment included 6 treatments, one being an un-amended (UN) control with addition of five OAs being raw mill mud (MM), composted mill mud (CM), high N compost (HC), rice husk biochar (RB), and raw mill mud plus rice husk biochar (MB). These OAs were incubated at 60, 75 and 90% water-filled pore space (WFPS) at 25°C with urea (equivalent to 200 kg N ha−1) added to the soils thirty days after the incubation commenced. Results showed WFPS did not influence CO2 emissions over the 60 days but the magnitude of emissions as a proportion of C applied was RB < CM < MB < HC emissions were significantly less in the clay soil compared to the sandy loam at all WFPS, and could be ranked RB emissions as a function of the dry matter and C/N ratio of the OAs, WFPS, and the soil CEC. Application of RB reduced N2O emissions by as much as 42-64% depending on WFPS. The reductions in both CO2 and N2O emissions after application of RB were due to a reduced bioavailability of C and not immobilisation of N. These findings show that the effect of OAs on soil GHG emissions can vary substantially depending on their chemical properties. OAs with a high availability of labile C and N can lead to elevated emissions of CO2 and N2O, while rice husk biochar showed potential in reducing overall soil GHG emissions.

Understanding public response to a congestion charge : a random-effects ordered logit approach

Public acceptance is consistently listed as having an enormous impact on the implementation and success of a congestion charge scheme. This paper investigates public acceptance of such a scheme in Australia. Surveys were conducted in Brisbane and Melbourne, the two fastest growing Australian cities. Using an ordered logit modeling approach, the survey data including stated preferences were analyzed to pinpoint the important factors influencing people’s attitudes to a congestion charge and, in turn, to their transport mode choices. To accommodate the nature of, and to account for the resulting heterogeneity of the panel data, random effects were considered in the models. As expected, this study found that the amount of the congestion charge and the financial benefits of implementing it have a significant influence on respondents’ support for the charge and on the likelihood of their taking a bus to city areas. However, respondents’ current primary transport mode for travelling to the city areas has a more pronounced impact. Meanwhile, respondents’ perceptions of the congestion charge’s role in protecting the environment by reducing vehicle emissions, and of the extent to which the charge would mean that they travelled less frequently to the city for shopping or entertainment, also have a significant impact on their level of support for its implementation. We also found and explained notable differences across two cities. Finally, findings from this study have been fully discussed in relation to the literature.

Exploring location-dependency in process modeling

Purpose – Context-awareness has emerged as an important principle in the design of flexible business processes. The goal of the research is to develop an approach to extend context-aware business process modeling toward location-awareness. The purpose of this paper is to identify and conceptualize location-dependencies in process modeling. Design/methodology/approach – This paper uses a pattern-based approach to identify location-dependency in process models. The authors design specifications for these patterns. The authors present illustrative examples and evaluate the identified patterns through a literature review of published process cases. Findings – This paper introduces location-awareness as a new perspective to extend context-awareness in BPM research, by introducing relevant location concepts such as location-awareness and location-dependencies. The authors identify five basic location-dependent control-flow patterns that can be captured in process models. And the authors identify location-dependencies in several existing case studies of business processes. Research limitations/implications – The authors focus exclusively on the control-flow perspective of process models. Further work needs to extend the research to address location-dependencies in process data or resources. Further empirical work is needed to explore determinants and consequences of the modeling of location-dependencies. Originality/value – As existing literature mostly focusses on the broad context of business process, location in process modeling still is treated as “second class citizen” in theory and in practice. This paper discusses the vital role of location-dependencies within business processes. The proposed five basic location-dependent control-flow patterns are novel and useful to explain location-dependency in business process models. They provide a conceptual basis for further exploration of location-awareness in the management of business processes.

Maternal concern about her toddler undereating and becoming underweight and use of feeding practices - analysis of the NOURISH and SAIDI cohorts using structural equation modeling

The chubby baby who eats well is desirable in our culture. Perceived low weight gains and feeding concerns are common reasons mothers seek advice in the early years. In contrast, childhood obesity is a global public health concern. Use of coercive feeding practices, prompted by maternal concern about weight, may disrupt a child’s innate self regulation of energy intake, promoting overeating and overweight. This study describes predictors of maternal concern about her child undereating/becoming underweight and feeding practices. Mothers in the control group of the NOURISH and South Australian Infants Dietary Intake studies (n = 332) completed a self-administered questionnaire when the child was aged 12–16 months. Weight-for-age z-score (WAZ)was derived from weight measured by study staff. Mean age (SD) was 13.8 (1.3) months, mean WAZ (SD), 0.58 (0.86) and 49% were male. WAZ and two questions describing food refusal were combined in a structural equation model with four items from the Infant feeding Questionnaire (IFQ) to form the factor ‘Concern about undereating/weight’. Structural relationships were drawn between concern and IFQ factors ‘awareness of infant’s hunger and satiety cues’, ‘use of food to calm infant’s fussiness’ and ‘feeding infant on a schedule’, resulting in a model of acceptable fit. Lower WAZ and higher frequency of food refusal predicted higher maternal concern. Higher maternal concern was associated with lower awareness of infant cues (r = −.17, p = .01) and greater use of food to calm (r = .13, p = .03). In a cohort of healthy children, maternal concern about undereating and underweight was associated with practices that have the potential to disrupt self-regulation.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Empirical vehicle-to-vehicle pathloss modeling in highway, suburban and urban environments at 5.8GHz

In this paper, we present a pathloss characterization for vehicle-to-vehicle (V2V) communications based on empirical data collected from extensive measurement campaign performed under line-of-sight (LOS), non-line-of-sight (NLOS) and varying traffic densities. The experiment was conducted in three different V2V propagation environments: highway, suburban and urban at 5.8GHz. We developed pathloss models for each of the three different V2V environments considered. Based on a log-distance power law model, the values for the pathloss exponent and the standard deviation of shadowing were reported. The average pathloss exponent ranges from 1.77 for highway, 1.68 for the urban to 1.53 for the suburban environment. The reported results can contribute to vehicular network (VANET) simulators and can be used by system designers to develop, evaluate and validate new protocols and system designs under realistic propagation conditions.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.