An integrated optimisation platform for sustainable resource and infrastructure planning

It is crucial for sustainable planning to consider broad environmental and social dimensions and systemic implications of new infrastructure to build more resilient societies, reduce poverty, improve human well-being, mitigate climate change and address other global change processes. This article presents resilience.io, 2 a platform to evaluate new infrastructure projects by assessing their design and effectiveness in meeting growing resource demands, simulated using Agent-Based Modelling due to socio-economic population changes. We then use Mixed-Integer Linear Programming to optimise a multi-objective function to find cost-optimal solutions, inclusive of environmental metrics such as greenhouse gas emissions. The solutions in space and time provide planning guidance for conventional and novel technology selection, changes in network topology, system costs, and can incorporate any material, waste, energy, labour or emissions flow. As an application, a use case is provided for the Water, Sanitation and Hygiene (WASH) sector for a four million people city-region in Ghana

Water and energy systems in sustainable city development: a case of Sub-saharan Africa

Current urban water and energy systems are expanding while increasing attention is paid to their social, economic and environmental impacts. As a research contribution that can support real-world decision making and transitions to sustainable cities and communities, we have built a model-based and data-driven platform combining comprehensive database, agent-based simulation and resource technology network optimization for system level water and energy planning. Several use cases are demonstrated based on the Greater Accra Metropolitan Area (GAMA) city-region in Ghana, as part of the Future Cities Africa (FCA) project. The outputs depict an overall resource landscape of the studied urban area, but also provide the energy, water, and other resource balance of supply and demand from both macro and micro perspectives, which is used to propose environmental friendly and cost effective sustainable city development strategies. This work is to become a core component of the resilience.io platform as an open-source integrated systematic tool gathering social, environmental and economic data to inform urban planning, investment and policy-making for city-regions globally

An agent-based decision support framework for a prospective analysis of transport and heat electrification in urban areas

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users’ behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents’ behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities

Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning

Advanced AODV approach for efficient detection and mitigation of wormhole attack in MANET

Wireless Communication is an inevitable part of Smart Home domain. A Mobile Ad-Hoc Network (MANET) is defined as an arrangement of wireless mobile nodes which creates a temporary network for the communication. MANET suffers from both kinds of attacks, active and passive attacks at all the layers of the network model. The lacks of security measures of routing protocols allow attackers to intrude the network. Wormhole, the attack is generated by tunnels creation and it results in complete disruption of routing paths on MANET. The proposed security approach is to detect and mitigate wormhole attack. It is secured Ad hoc on demand distance vector (AODV) approach which efficiently finds wormhole attack present in a MANET and Digital signature is used to prevent it. This approach is based on a calculation of tunneling time taken by tunnel to analyze the behavior of wormhole. Afterward, it decides some static threshold value. Based upon this tunneling time and threshold value, it decides whether given node is wormhole node or trustworthy node. A digital signature and hash chain algorithm is applied to mitigate the wormhole node

The local economic development processes in low-income countries: the case of the metropolis of Chegutu in Zimbabwe

Local authorities are widely regarded as catalysts accelerating localised processes of economic development in industrialised countries but in low-income countries they are perceived as dysfunctional, inefficient and ineffective in meeting and addressing societal demands. This abstract view is however, not grounded in empirical research. As such, utilising the case of the metropolis of Chegutu a survey was designed to empirically explicate the economic processes militating its economic development. The findings are useful to policy-makers, local government authorities and management scholars. The study's unique contribution lies in its examination of the processes of local economic development in a low-income country