Solidification/stabilisation of soil contaminated with metal: a review

Solidification/stabilisation (S/S) is generically defined as a chemical and physical alteration technique of reducing the mobility as well as solubility of contaminants in wastes in order to convert them into chemically inert form. The technique is specifically developed to confine the movement of contaminants in wastes so that their concentrations in the surrounding environment (e.g. subsurface soil matrices and groundwater) will not exceed stipulated environmental regulatory levels. This technique necessitates application of cementitious materials such as cement which also provides a favorable solidification effect on the stabilised wastes so that the end product can be easily transported to disposal sites or reused as construction materials. This paper reviews the S/S technology as applied to contaminated soil treatment with emphasis on its chemical binder systems, mechanisms, interferences and post-treatment leaching tests. S/S is an important soil contamination remediation technology as evident by its simplicity, technical and cost-effectiveness

Preliminary results on literature review skills of students doing capstone projects

[Abstract]: A survey was carried out to investigate literature review skills of students doing their final year research projects. This survey was given before intervention to their literature review skills. Their literature review skills revealed by this survey were compared with that of a post-intervention survey, which was in turn compared with the literature review skills revealed in their intermittent reports. From the pre- and post-intervention surveys, it seems that the intervention workshops work and the percentage of students understanding how to conduct a literature review has improved from 11% to 78%. However, the intervention was not so successful if the literature review knowledge revealed by the project appreciation was compared with that of pre-intervention survey

Key Challenges and Opportunities in Hull Form Design Optimisation for Marine and Offshore Applications

New environmental regulations and volatile fuel prices have resulted in an ever-increasing need for reduction in carbon emission and fuel consumption. Designs of marine and offshore vessels are more demanding with complex operating requirements and oil and gas exploration venturing into deeper waters and hasher environments. Combinations of these factors have led to the need to optimise the design of the hull for the marine and offshore industry. The contribution of this paper is threefold. Firstly, the paper provides a comprehensive review of the state-ofthe- art techniques in hull form design. Specifically, it analyses geometry modelling, shape transformation, optimisation and performance evaluation. Strengths and weaknesses of existing solutions are also discussed. Secondly, key challenges of hull form optimisation specific to the design of marine and offshore vessels are identified and analysed. Thirdly, future trends in performing hull form design optimisation are investigated and possible solutions proposed. A case study on the design optimisation of bulbous bow for passenger ferry vessel to reduce wavemaking resistance is presented using NAPA software. Lastly, main issues and challenges are discussed to stimulate further ideas on future developments in this area, including the use of parallel computing and machine intelligence

Establishing links between organizational climate, employee well-being and historical patient outcomes

This research undertaken in collaboration with Queensland Health analysed the links between dimensions of workplace climate/employee well-being contained in a number of Queensland Health databases, including the Patient Satisfaction Survey, the Clinical Incident database, the compliments and complaints database, the Variable Life Adjusted Display (VLAD) Database and the Better Workplaces Staff Opinion Survey database. Queensland Health sought to identify in what ways workplace climate is related to patient outcomes using existing datasets collected within the Queensland Health Centre for Healthcare Improvement. The process of establishing links involved matching aggregated data for specific facilities (where possible), or failing that, larger facilities (e.g. Hospital), or the Health Service District. Once the datasets had been matched on location or facility, correlations were calculated between the aggregated scores. The results demonstrated links between the data sets. These links showed that a better workplace climate is associated with greater reported numbers of clinical incidents, especially “no harm” clinical incidents. There was also a link between workplace climate and patient compliments/complaints which show that unsolicited compliments received from patients and their families are clearly related to a number of positive aspects of workplace climate (workplace morale, role clarity, and appraisal and recognition) and individual morale. The results linking workplace climate and patient satisfaction showed that there is a strong positive relationship between overall patient satisfaction and role clarity, and a negative relationship between overall patient satisfaction and both workplace distress and excessive work demands. While these results relate to historical data and therefore should not be construed to reflect the current state of operation within Queensland Health, they are still indicative of some very important relationships. This is the first study to demonstrate that more positive clinical management practices, better perceptions of the workplace climate and better employee well-being are a reflection of a better incident reporting and learning culture in a health care organization, ultimately resulting in improved patient outcomes

A CMOS-Based Lab-on-Chip Array for Combined Magnetic Manipulation and Opto-Chemical Sensing

Accepted versio

Bow-Tie Microstrip Antenna Design

In this paper, the bow-tie microstrip antennas have been designed with two different angles of 40° and 80°. An investigaton on the effect of the angle to the return loss and radiation patterns had been carried out. The impedance matching network with the niicrostrip transmission line feeding was used in this study. Simulation and measurement results for the return loss and radiation patterns were presented

Reply to "Comment on 'Universal Behavior of Load Distribution in Scale-Free Networks'"

Reply to "Comment on 'Universal Behavior of Load Distribution in Scale-Free Networks.'"Comment: 1 page, 1 figur

Germline genetic variation in prostate susceptibility does not predict outcomes in the chemoprevention trials PCPT and SELECT

Background The development of prostate cancer can be influenced by genetic and environmental factors. Numerous germline SNPs influence prostate cancer susceptibility. The functional pathways in which these SNPs increase prostate cancer susceptibility are unknown. Finasteride is currently not being used routinely as a chemoprevention agent but the long term outcomes of the PCPT trial are awaited. The outcomes of the SELECT trial have not recommended the use of chemoprevention in preventing prostate cancer. This study investigated whether germline risk SNPs could be used to predict outcomes in the PCPT and SELECT trial. Methods Genotyping was performed in European men entered into the PCPT trial (n = 2434) and SELECT (n = 4885). Next generation genotyping was performed using Affymetrix® Eureka™ Genotyping protocols. Logistic regression models were used to test the association of risk scores and the outcomes in the PCPT and SELECT trials. Results Of the 100 SNPs, 98 designed successfully and genotyping was validated for samples genotyped on other platforms. A number of SNPs predicted for aggressive disease in both trials. Men with a higher polygenic score are more likely to develop prostate cancer in both trials, but the score did not predict for other outcomes in the trial. Conclusion Men with a higher polygenic risk score are more likely to develop prostate cancer. There were no interactions of these germline risk SNPs and the chemoprevention agents in the SELECT and PCPT trials

Predicting Seismic Liquefaction Using Neural Networks

Neural networks have emerged as a powerful computational technique for modeling nonlinear multivariate relationships. The neural network is a product of artificial intelligence research. This paper examines the feasibility of using neural networks for assessing liquefaction potential, from actual field records. The paper starts with a brief overview of the basic architecture and concepts of neural networks. The application of the neural network methodology to evaluate seismic liquefaction potential is then presented