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In many parts of the world, a rapid urbanization process is taking place at an unprecedented scale, and its drastic impacts on societies and the environment are evident. To combat the externalities of such rapid, and to a degree uncontrolled, development, many cities around the globe introduced various urban growth management policies. However, policy making-to provide sustainable outcomes, while generating growth opportunities-has been a daunting task for urban administrators. To ease the task, scenario-based planning methods are introduced to produce alternative visions for managing urban growth in sustainable ways by incorporating various socio-environmental issues. However, even though modelling urban growth and associated impacts based on these scenarios have emerged to strengthen and quantify the future of urban policies and related planning actions, this process has a number of glitches. Major issues include the uncertainties associated with the selection of suitable methods to generate scenarios, identify indicators to be used to assess scenarios, evaluate scenarios to prioritize for policy formulation, and assess the impacts of policy scenarios. This paper aims to address the challenge of developing suitable policy scenarios for sustainable urban growth. As for the methodological approach, the study undertakes a thorough review of the literature and current practices, and conducts a two-round Delphi survey-involving experts from public, private and academic sectors specialized in the fields of urban planning, environmental planning, social planning, transportation modelling, and economic development. The expert driven policy scenarios are validated in a local context by comparing findings against the policy options as proposed in the South East Queensland Regional Plan 2017 (Australia). The findings offer valuable guidelines for planners, modellers, and policy makers in adopting suitable methods, indicators, and policy priorities, and thus, easing the daunting task of generating sustainable policy solutions.

Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing diabetes in an individual effectively? To our knowledge, no study has attempted use of HMM to validate the performance of FDRSM. We used Electronic Medical Record (EMR) data, of 172,168 primary care patients to derive the 8-year risk of developing diabetes in an individual using HMM. The Area Under Receiver Operating Characteristic Curve (AROC) in our study sample of 911 individuals for whom all risk factors and follow up data were available is 86.9% compared to AROCs of 78.6% and 85% reported in a previously conducted validation study of FDRSM in the same Canadian population and the Framingham study respectively. These results demonstrate that the discrimination capability of our proposed HMM is superior to the validation study conducted using the FDRSM in a Canadian population and in the Framingham population. We conclude that HMM is capable of identifying patients at increased risk of developing diabetes within the next 8-years.

Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently no non-invasive ATP III criteria based prediction method is available to diagnose NAFLD risk. Firstly, the objective of this research is to develop machine learning based method in order to identify individuals at an increased risk of developing NAFLD using risk factors of ATP III clinical criteria updated in 2005 for Metabolic Syndrome (MetS). Secondly, to validate the relative ability of quantitative score defined by Italian Association for the Study of the Liver (IASF) and guideline explicitly defined for the Canadian population based on triglyceride thresholds to predict NAFLD risk. We proposed a Decision Tree based method to evaluate the risk of developing NAFLD and its progression in the Canadian population, using Electronic Medical Records (EMRs) by exploring novel risk factors for NAFLD. Our results show proposed method could potentially help physicians make more informed choices about their management of patients with NAFLD. Employing the proposed application in ordinary medical checkup is expected to lessen healthcare expenditures compared with administering additional complicated test.

The objective of this inductive research was to investigate: 1) the relationship between diabetes mellitus and individual risk factors of metabolic syndrome (MetS), in a non-conservative setting; 2) the prediction of future onset of diabetes using relevant risk factors of MetS; and 3) to investigate the relative performance of machine learning methods when data sampling techniques are used to generate balanced training sets. The dataset used in this research contains 667 907 records for a period ranging from 2003 to 2013. Quantifying the contribution of individual risk factors of MetS in the development of diabetes in a non-conservative setting logistic regression analysis was performed. Our analyses contradict the view that diabetes is commonly associated with low levels of high-density lipoprotein (HDL). Instead, our results demonstrate that the increased levels of HDL are positively correlated with diabetes onset, particularly in women. We also proposed J48 decision tree and Naïve Bayes methods for prediction of future onset of diabetes using relevant risk factors obtained from logistic regression analysis, over balanced and unbalanced datasets. The results demonstrated the supremacy of Naïve Bayes with K-medoids undersampling technique as compared to random under-sampling, oversampling, and no sampling. It is achieved on average 79% receiver operating characteristic performance with the increased true positive rate. The results of this paper suggest further research to clarify the pathophysiological significance of HDL and pathways in the development of diabetes.

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The proximate mineral composition, amino acid profile and physicochemical characteristics of three new chickpea cultivars (two Desi: NIFA‐88, NIFA‐95, and one Kabuli: Hassan‐2k) grown in the North West Frontier Province of Pakistan were studied, in order to assess their role in human nutrition. It was found that chickpea is a rich source of protein and minerals. The protein content (22.89–24.82%) of chickpea was much higher than that of cereals (wheat and maize), and comparable to other legumes. Mineral composition of chickpea cultivars showed that they contribute sufficient amount of Ca, P, K, Cu, Zn and Mg in human diets to meet the recommended dietary allowance. The essential amino acid leucine (8.7% of protein) was found in highest concentration, followed by arginine (8.3% of protein) and lysine (7.2% of protein) in the chickpea cultivars. In terms of physical characteristics, more variability was observed in seed size and seed volume than in hydration and swelling capacities, cooking time and hydration and swelling indices. Phytic acid content ranged from 132.3 to 170.7 mg/100 g of dried sample. The higher amount was recorded in Desi than in the Kabuli cultivar of chickpea. It was concluded that chickpea may improve the nutritional value of a cereal‐based diet.

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