Water sharing within the states/provinces of a country and cross-border is unavoidable. Conflicts between the sharing entities might turn more severe due to additional dependency on water, growing population, and reduced availability as a result of climate change at many locations. Pakistan, being an agricultural country, is severely water stressed and heading toward a worsening situation in the near future. Pakistan is heading toward water scarcity as water availability in the Indus basin is becoming critical. Being a downstream riparian of India and Afghanistan in the Indus basin, water availability depends on the releases of water from both countries. The Indus Water Treaty is governing the water distribution rights between India and Pakistan. However, there exists no proper agreement between Pakistan and Afghanistan and the construction of new dams on the Kabul River is another threat to water availability to Pakistan. Correct implementation of the Indus Water Treaty with India is required, together with an effective agreement with Afghanistan about the water sharing. In addition to water shortage, poor management of water resources, inequitable sharing of water, lack of a systematic approach, old-fashioned irrigation practices, and growing agricultural products with large water footprints are all exacerbating the problem. The water shortage is now increasingly countered by the use of groundwater. This sudden high extraction of groundwater is causing depletion of the groundwater table and groundwater quality issues. This water shortage is exacerbating the provincial conflicts over water, such as those between Punjab and Sindh provinces. At one end, a uniform nationwide water allocation policy is required. At the same time, modern irrigation techniques and low-water-footprint agricultural products should be promoted. A fair water-pricing mechanism of surface water and groundwater could be an effective measure, whereas a strict policy on groundwater usage is equally important. Political will and determination to address the water issues are required. The solutions must be based on transparency and equity, by using engineering approaches, combined with comprehensive social support. To develop a comprehensive water strategy, a dedicated technopolitical institute to strengthen the capabilities of nationwide expertise and address the issues on a regular basis is required to overcome the complex and multidimensional water-related problems of the country.

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A Smart City is a solution to the problems caused by increasing urbanization. Australia has demonstrated a strong determination for the development of Smart Cities. However, the country has experienced uneven growth in its urban development. The purpose of this study is to compare and identify the smartness of major Australian cities to the level of development in multi-dimensions. Eventually, the research introduces the openings to make cities smarter by identifying the focused priority areas. To ensure comprehensive coverage of all aspects of the smart city’s performance, 90 indicators were selected to represent 26 factors and six components. The results of the assessment endorse the impacts of recent government actions taken in different urban areas towards building smarter cities. The research has pointed out the areas of deficiencies for underperforming major cities in Australia. Following the results, appropriate recommendations for Australian cities are provided to improve the city’s smartness.

BackgroundObstructive sleep apnea (OSA) is a public health problem. Detailed analysis of the para-pharyngeal fat pads can help us to understand the pathogenesis of OSA and may mediate the intervention of this sleeping disorder. A reliable and automatic para-pharyngeal fat pads segmentation technique plays a vital role in investigating larger data bases to identify the anatomic risk factors for the OSA.MethodsOur research aims to develop a context-based automatic segmentation algorithm to delineate the fat pads from magnetic resonance images in a population-based study. Our segmentation pipeline involves texture analysis, connected component analysis, object-based image analysis, and supervised classification using an interactive visual analysis tool to segregate fat pads from other structures automatically.ResultsWe developed a fully automatic segmentation technique that does not need any user interaction to extract fat pads. Our algorithm is fast enough that we can apply it to population-based epidemiological studies that provide a large amount of data. We evaluated our approach qualitatively on thirty datasets and quantitatively against the ground truths of ten datasets resulting in an average of approximately 78% detected volume fraction and a 79% Dice coefficient, which is within the range of the inter-observer variation of manual segmentation results.ConclusionThe suggested method produces sufficiently accurate results and has potential to be applied for the study of large data to understand the pathogenesis of the OSA syndrome.

Background: Obstructive sleep apnea (OSA) is a chronic sleeping disorder. The analysis of pharynx and its surrounding tissues can play a vital role in understanding the pathogenesis of OSA. Classification of pharynx is a crucial step in the analysis of OSA. Objective: An automatic pharyngeal classification from magnetic resonance images (MRI) and the influence of different features can help in analyzing the pharynx anatomy. However, the state-of-the-art classifiers do not provide any insight regarding the features’ selection and their influence. Methods: A visual analysis-based classifier is developed to classify the pharynx from MRI datasets. The classification pipeline consists of different stages including pre-processing to select the initial candidates, extraction of categorical and numerical features to form a multidimensional features space, and a supervised classifier trained by using visual analytics and silhouette coefficient to classify the pharynx. Results: The pharynx is classified automatically and gives an approximately 86% Jaccard coefficient by evaluating the classifier on different MRI datasets. The expert’s knowledge can be utilized to select the optimal features and their corresponding weights during the training phase of the classifier. Conclusion: The proposed classifier is accurate and more efficient in terms of computational cost. It provides additional insight to better understand the influence of different features individually and collectively. It finds its applications in epidemiological studies where large datasets need to be analyzed.