Can urban ponds help tackle domestic water scarcity and build resilience?

For decades to come, cost-effective and environmentally appropriate water systems will be a priority for managing water scarcity and building resilience in the rapidly expanding cities and city regions of South Asia. This study initiates a research into urban local ponds and the potential of linking them with water systems and build resilience. A framework of questions guided the research with reference to ponds and prevalent water systems in South Asian cities and city regions. The wider issues of water stress in South Asian cities and the general limitations of prevalent water supply systems were studied through the lens of a literature review. The paper then draws upon observations in three South Asian cities. The research showed that despite policy support for local rainwater capture, groundwater is over-exploited and urban local ponds (and tanks) have not been integrated with urban water provision schemes, particularly in recent decades. It was concluded that local urban ponds can facilitate resilient water-supply provision by making them an integral part of the urban waterscape. This paper highlights a multitude of benefits that ponds can potentially bring to urban resilience, in particular affordable and accessible water provision with low environmental footprint, managing climate shocks or stresses, biodiversity restoration in urban areas as well as potentially generating new skills and livelihoods for communities. The overall suggestion is that local urban ponds should be networked into the water provision for cities and their wider region, thereby linking to wider arrangements for urban and regional governance and resilience

Consolidated List of Requirements

This document is a consolidated catalogue of requirements for the Electronic Health Care Record (EHCR) and Electronic Health Care Record Architecture (EHCRA), gleaned largely from work done in the EU Framework III and IV programmes and CEN, but also including input from other sources including world-wide standardisation initiatives. The document brings together the relevant work done into a classified inventory of requirements to inform the on-going standardisation process as well as act as a guide to future implementation of EHCRA-based systems. It is meant as a contribution both to understanding of the standard and to the work that is being considered to improve the standard. Major features include the classification into issues affecting the Health Care Record, the EHCR, EHCR processing, EHCR interchange and the sharing of health care information and EHCR systems. The principal information sources are described briefly. It is offered as documentation that is complementary to the four documents of the ENV 13606 Parts I-IV produced by CEN Pts 26,27,28,29. The requirements identified and classified in this deliverable are referenced in other deliverables

Learning Opposites Using Neural Networks

Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using "opposition-based learning" (OBL). Two types of the "opposites" have been defined in the literature, namely \textit{type-I} and \textit{type-II}. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture the "oppositeness" in the output space. In fact, type-I opposites are considered a special case of type-II opposites where inputs and outputs have a linear relationship. However, in many real-world problems, inputs and outputs do in fact exhibit a nonlinear relationship. Therefore, type-II opposites are expected to be better in capturing the sense of "opposition" in terms of the input-output relation. In the absence of any knowledge about the problem at hand, there seems to be no intuitive way to calculate the type-II opposites. In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs). We first perform \emph{opposition mining} on the sample data, and then use the mined data to learn the relationship between input $x$ and its opposite $\breve{x}$. We have validated our algorithm using various benchmark functions to compare it against an evolving fuzzy inference approach that has been recently introduced. The results show the better performance of a neural approach to learn the opposites. This will create new possibilities for integrating oppositional schemes within existing algorithms promising a potential increase in convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

Magnetic resonance spectroscopy in ring enhancing lesions

We report a 4 year old girl with ring enhancing lesions in brain CT, initially diagnosed as neurocysticercosis but did not respond to cysticidal therapy. A Magnetic resonance spectropscopy (MRS) revealed lipid peaks suggestive of tuberculoma which was successfully treated with antituberculosis therapy. This report highlights the role of MRS in the diagnosis of ring enhancing lesios