Handbook on Climate Change and Disaster Resilient Water, Sanitation and Hygiene Practices

This handbook was developed to help governmental and non-governmental agencies involved in safe water delivery, sanitation hygiene at local level, union and ward disaster management committees, to enhance their respective capacities to cope with climate change and disaster risks. It considers the rural context of Bangladesh and provides field-level workers and practitioners practical ideas about water supply, sanitation and hygiene practices in the context of climate change and disaster risk

CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.Comment: 8 pages, 2 figures, 1 tabl

Future-proofing the state: managing risks, responding to crises and building resilience

Summary: This book focuses on the challenges facing governments and communities in preparing for and responding to major crises — especially the hard to predict yet unavoidable natural disasters ranging from earthquakes and tsunamis to floods and bushfires, as well as pandemics and global economic crises. Future-proofing the state and our societies involves decision-makers developing capacities to learn from recent ‘disaster’ experiences in order to be better placed to anticipate and prepare for foreseeable challenges. To undertake such futureproofing means taking long-term (and often recurring) problems seriously, managing risks appropriately, investing in preparedness, prevention and mitigation, reducing future vulnerability, building resilience in communities and institutions, and cultivating astute leadership. In the past we have often heard calls for ‘better future-proofing’ in the aftermath of disasters, but then neglected the imperatives of the message. Future-Proofing the State is organised around four key themes: how can we better predict and manage the future; how can we transform the short-term thinking shaped by our political cycles into more effective long-term planning; how can we build learning into our preparations for future policies and management; and how can we successfully build trust and community resilience to meet future challenges more adequately

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Business Continuity and Disaster Recovery Plan for Information Security

Business continuity planning and Disaster recovery planning are the most crucial elements of a business but are often ignored. Businesses must make a well-structured plan and document for disaster recovery and business continuation, even before a catastrophe occurs. Disasters can be short or may last for a long time, but when an organization is ready for any adversity, it thrives hard and survives. This paper will clearly distinguish the difference between disaster recovery plan and business continuity plan, will describe the components of each plan and finally, will provide an approach that organizations can follow to make better contingency plan so that they will not go out of business when something unexpected happens. This paper will put forward a list of recommendations that an organization can follow to maintain enough strength and resources to react and come out of the crisis successfully

The Global Risks Report 2016, 11th Edition

Now in its 11th edition, The Global Risks Report 2016 draws attention to ways that global risks could evolve and interact in the next decade. The year 2016 marks a forceful departure from past findings, as the risks about which the Report has been warning over the past decade are starting to manifest themselves in new, sometimes unexpected ways and harm people, institutions and economies. Warming climate is likely to raise this year's temperature to 1° Celsius above the pre-industrial era, 60 million people, equivalent to the world's 24th largest country and largest number in recent history, are forcibly displaced, and crimes in cyberspace cost the global economy an estimated US$445 billion, higher than many economies' national incomes. In this context, the Reportcalls for action to build resilience – the "resilience imperative" – and identifies practical examples of how it could be done.The Report also steps back and explores how emerging global risks and major trends, such as climate change, the rise of cyber dependence and income and wealth disparity are impacting already-strained societies by highlighting three clusters of risks as Risks in Focus. As resilience building is helped by the ability to analyse global risks from the perspective of specific stakeholders, the Report also analyses the significance of global risks to the business community at a regional and country-level

Autonomous Inspections of Utility Networks

Field inspections of utility network assets are a critical aspect of maintaining the quality and integrity of services provided by a utility. Currently, such inspections are performed largely by human inspectors. Such manual inspections are time-consuming, expensive, potentially dangerous to the inspectors and the surrounding public, subject to human error, disruptive to neighboring communities, and lack the speedy response needed in a disaster scenario. This disclosure describes a machine-based inspection mechanism for utility networks, based on, for example, autonomous or remotely-operated drones with the capability to perform intricate inspections at difficult-to-reach regions and heights. The inspection tasks including, for example, navigation routes, assets and parameters to be inspected, etc. are determined, for example, by a machine learner that has access to real-time and historical data from a variety of relevant sources