Removal of toxic arsenic and antimony from groundwater Spiro Tunnel Bulkhead in Park City Utah using colloidal iron hydroxide : comparison with reverse ssmosis

Verification testing of two model technologies in pilot scale to remove arsenic and antimony based on reverse osmosis and chemical coagulation/filtration systems was conducted in Spiro Tunnel Water Filtration Plant located in Park City, Utah, US. The source water was groundwater in abandoned silver mine, naturally contaminated by 60-80 ppb of arsenic and antimony below 10 ppb. This water represents one of the sources of drinking water for Park City and constitutes about 44% of the water supply. The failure to remove antimony efficiently by coagulation/filtration (only 4.4% removal rate) under design conditions is discussed in terms of the chemistry differences between Sb (III, V) and As (III, V). Removal of Sb(V) at pH > 7, using coagulation/filtration technology, requires much higher (50 to 80 times) concentration of iron (III) than As. The stronger adsorption of arsenate over a wider pH range can be explained by the fact that arsenic acid is tri-protic, whereas antimonic acid is monoprotic. This difference in properties of As(V) and Sb(V) makes antimony (V) more difficult to be efficiently removed in low concentrations of iron hydroxide and alkaline pH waters, especially in concentration of Sb < 10 ppb.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Right to the city, desire for the suburb?

The 2000s have been a lively decade for cities. The Worldwatch Institute estimated that 2007 was the first year in human history that more people worldwide lived in cities than the countryside. Globalisation and new digital media technologies have generated the seemingly paradoxical outcome that spatial location came to be more rather than less important, as combinations of firms, industries, cultural activities and creative talents have increasingly clustered around a select node of what have been termed “creative cities,” that are in turn highly networked into global circuits of economic capital, political power and entertainment media. Intellectually, the period has seen what the UCLA geographer Ed Soja refers to as the spatial turn in social theory, where “whatever your interests may be, they can be significantly advanced by adopting a critical spatial perspective”. This is related to the dynamic properties of socially constructed space itself, or what Soja terms “the powerful forces that arise from socially produced spaces such as urban agglomerations and cohesive regional economies,” with the result that “what can be called the stimulus of socio-spatial agglomeration is today being assertively described as the primary cause of economic development, technological innovation, and cultural creativity”

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.

Darkness and disaster in the city

People all over the world are regularly hit by floods, cyclones, and other natural disasters. Many use smart phones and social media to stay connected, seek help, improvise, and cope with crises or challenging situations. This column discusses these practices after dark or during disasters to unveil challenges and opportunities for innovative designs that increase resilience and safety.

Non-redundant sequential association rule mining based on closed sequential patterns

In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the data is naturally in the form of sequences. People have taken great interest in analysing the sequential data and finding the inherent characteristics or relationships within the data. Sequential association rule mining is one of the possible methods used to analyse this data. As conventional sequential association rule mining very often generates a huge number of association rules, of which many are redundant, it is desirable to find a solution to get rid of those unnecessary association rules. Because of the complexity and temporal ordered characteristics of sequential data, current research on sequential association rule mining is limited. Although several sequential association rule prediction models using either sequence constraints or temporal constraints have been proposed, none of them considered the redundancy problem in rule mining. The main contribution of this research is to propose a non-redundant association rule mining method based on closed frequent sequences and minimal sequential generators. We also give a definition for the non-redundant sequential rules, which are sequential rules with minimal antecedents but maximal consequents. A new algorithm called CSGM (closed sequential and generator mining) for generating closed sequences and minimal sequential generators is also introduced. A further experiment has been done to compare the performance of generating non-redundant sequential rules and full sequential rules, meanwhile, performance evaluation of our CSGM and other closed sequential pattern mining or generator mining algorithms has also been conducted. We also use generated non-redundant sequential rules for query expansion in order to improve recommendations for infrequently purchased products.

Fixing the city one photo at a time : mobile logging of maintenance requests

We have designed a mobile application that takes advantage of the built-in features of smart phones such as camera and GPS that allow users to take geo-tagged photos while on the move. Urban residents can take pictures of broken street furniture and public property requiring repair, attach a brief description, and submit the information as a maintenance request to the local government organisation of their city. This paper discusses the design approach that led to the application, highlights a built-in mechanism to elicit user feedback, and evaluates the progress to date with user feedback and log statistics. It concludes with an outlook highlighting user requested features and our own design aspirations for moving from a reporting tool to a civic engagement tool.

The effect of various temperature indicators on different mortality categories in a subtropical city of Brisbane, Australia.

BACKGROUND: The relationship between temperature and mortality has been explored for decades and many temperature indicators have been applied separately. However, few data are available to show how the effects of different temperature indicators on different mortality categories, particularly in a typical subtropical climate. OBJECTIVE: To assess the associations between various temperature indicators and different mortality categories in Brisbane, Australia during 1996-2004. METHODS: We applied two methods to assess the threshold and temperature indicator for each age and death groups: mean temperature and the threshold assessed from all cause mortality was used for all mortality categories; the specific temperature indicator and the threshold for each mortality category were identified separately according to the minimisation of AIC. We conducted polynomial distributed lag non-linear model to identify effect estimates in mortality with one degree of temperature increase (or decrease) above (or below) the threshold on current days and lagged effects using both methods. RESULTS: Akaike's Information Criterion was minimized when mean temperature was used for all non-external deaths and deaths from 75 to 84 years; when minimum temperature was used for deaths from 0 to 64 years, 65-74 years, ≥ 85 years, and from the respiratory diseases; when maximum temperature was used for deaths from cardiovascular diseases. The effect estimates using certain temperature indicators were similar as mean temperature both for current day and lag effects. CONCLUSION: Different age groups and death categories were sensitive to different temperature indicators. However, the effect estimates from certain temperature indicators did not significantly differ from those of mean temperature.

Regulating greenhouse gas emissions from coal mining activities in the context of climate change

Current approaches to the regulation of coal mining activities in Australia have facilitated the extraction of substantial amounts of coal and coal seam gas. The regulation of coal mining activities must now achieve the reduction or mitigation of greenhouse gas emissions in order to address the challenge of climate change and achieve ecologically sustainable development. Several legislative mechanisms currently exist which appear to offer the means to bring about the reduction or mitigation of greenhouse gas emissions from coal mining activities, yet Australia’s emissions from coal mining continue to rise. This article critiques these existing legislative mechanisms and presents recommendations for reform.

Knowledge-based urban development processes of an emerging knowledge city : Brisbane, Australia

In the global knowledge economy, to attract and retain knowledge-intensive industries and workers, cities produce various development strategies. Such strategising is an important development mechanism for cities to complete their transformation into knowledge cities. This paper discusses the critical connections between knowledge city foundations and integrated knowledge-based urban development strategies, and scrutinises Brisbane’s strategies in attracting and retaining investment and talent. The paper introduces a knowledge-based urban development assessment framework and uses this framework to provide a clearer understanding of Brisbane’s knowledge-based development processes and knowledge city transformation experience. The assessment framework particularly focuses on examining Brisbane’s four development processes, institutional, economic, socio-cultural and urban development, in detail. The findings reveal that although Brisbane is still in early stages of its transformation into a fully-fledged knowledge city, global orientation and achievements of Brisbane in strategising knowledge-based urban development are noteworthy.

Moving towards a knowledge city? Brisbane's experience in knowledge-based urban development

: In the global knowledge economy, knowledge-intensive industries and knowledge workers are extensively seen as the primary factors to improve the welfare and competitiveness of cities. To attract and retain such industries and workers, cities produce knowledge-based urban development strategies, where such strategising is also an important development mechanism for cities and their economies. This paper investigates knowledge-based urban development strategies of Brisbane, Australia that support generation, attraction, and retention of investment and talent. The paper puts forward a clear understanding on the policy frameworks, and relevant applications of Brisbane’s knowledge-based urban development experience in becoming a prosperous knowledge city, and concludes by providing invaluable insights and directions for other cities seeking knowledge-based urban development.

Contributions of knowledge-based foundations of universities in knowledge city formation : a Malaysian case study

Purpose – In the 21st Century, as knowledge, technology and education are widely accepted to play key roles in the local economic development, the importance of making space and place for knowledge production is, therefore, on the rise resulting many city administrations and urban policy-makers worldwide restructuring their cities to become highly competitive and creative. Consequently, this has led to a new type of city form, knowledge city, and a new approach in their development, knowledge-based urban development. In this context, knowledge-based foundations of universities are regarded as one of the key elements for knowledge-based urban development and knowledge city formation due to their ability to provide a strong platform for knowledge generation, marketing and transfer. This paper aims to investigate the role and importance of universities and their knowledge-based foundations in the context of developing countries, particularly in Malaysia, in building prosperous knowledge cities of the era of the knowledge economy. Design/Methodology/Approach – The main methodological techniques employed in this research includes: a thorough review of the literature on the role of universities in spatial and socio-economic development of cities; a best practice analysis and policy review of urban and regional development policies targeting to use of university clusters in leveraging knowledge-based development, and; a case study in Malaysia with a review of various policy documents and strategic plans of the local universities and local and state authorities, interviews with key actors, and a trend analysis of local socio-economic and spatial changes. Originality/Value – This paper reports the findings of a pioneering research on examining the role and impact of universities and their knowledge-based foundations, in the context of Malaysia, in building knowledge cities of the era of the knowledge economy. By undertaking a case study investigation in Bandar Seri Iskandar, which is a newly emerging Malaysian knowledge city, located in Perak, Malaysia, the paper sheds light on an important issue of the 21st Century of how universities contribute to the knowledge-based development of cities. Practical Implications – Universities with their rich knowledge-based foundations are increasingly being recognised as knowledge hubs, exercising a strong influence in the intellectual vitality of the city where they are embedded. This paper reveals that universities, in joint action with business and society at large, are necessary prerequisites for constructing and maintaining knowledge societies and, therefore, building prosperous knowledge cities. In light of the literature and case findings, the paper sheds light on the contribution of knowledge-based foundations of universities in knowledge city formation and provides generic recommendations for cities and regions seeking knowledge city transformation.

Measuring neighbourhood sustainability performance: an indexing model for Gold Coast City, Australia

The aim of this research is to develop an indexing model to evaluate sutainability performance of urban settings, in order to assess environmental impacts of urban development and to provide planning agencies an indexing model as a decision support tool to be used in curbing negative impacts of urban development. Indicator-based sustainability assessment is embraced as the method. Neigbourhood-level urban form and transport related indicators are derived from the literature by conducting a content analysis and finalised via a focus group meeting. The model is piloted on three suburbs of Gold Coast City, Australia. Final neighbourhood level sustainability index score was calculated by employing equal weighting schema. The results of the study show that indexing modelling is a reasonably practical method to measure and visualise local sustainability performance, which can be employed as an effective communication and decision making tool.

Interactions between pedestrians and cyclists in the city centre

The city centre represents a complex environment for cycling with large volumes of pedestrians and motorised vehicles and frequent signalised intersections. Much of the previous literature has focused on cyclist-motor vehicle interactions because of the safety implications for cyclists, but there is increasing concern from pedestrians about the threats they perceive from cyclists. In the absence of objective data, this has the potential to lead to restrictions on cyclist access and behaviour. This presentation reports the development of a method to study the extent of cycling in the city centre and the frequency and nature of interactions between cyclists and pedestrians. Queensland is one of the few Australian jurisdictions that permits adults to cycle on the footpath and this was also of interest. 1992 cyclists were observed at six locations in the Brisbane city centre, during 7-9am, 9-11am, 2-4pm and 4-6pm on four weekdays in October 2010. The majority (85.5%) of cyclists were male, and 21.8% rode on the footpath. Females were more likely to travel on the footpath than males. One or more pedestrians were within 1m for 18.1% of observed cyclists, and one or more pedestrians were within 5m for 39.1% of observed cyclists. There were few conflicts, defined as an occasion where if no one took evasive action a collision would occur, between cyclists and pedestrians or vehicles (1.1% and 0.6% respectively) but they were more common for adolescents and riders not wearing (or not fastening) helmets.