Case Studies of Real-Time Risk Management via Adjoint Algorithmic Differentiation (AAD)

2018 ◽  
pp. 339-370 ◽  
Author(s):  
Luca Capriotti ◽  
Jacky Lee
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Case Studies ◽  
Algorithmic Differentiation

A Pandemic of Violence? The Impact of COVID-19 on Conflict

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Sara M.T. Polo
Keyword(s):  
Spatial Distribution ◽  
Real Time ◽  
Case Studies ◽  
Armed Conflict ◽  
Event Data ◽  
Social Unrest ◽  
Johns Hopkins University ◽  
The World ◽  
Political Advantage ◽  
The Impact

AbstractThis article examines the impact and repercussions of the COVID-19 pandemic on patterns of armed conflict around the world. It argues that there are two main ways in which the pandemic is likely to fuel, rather than mitigate, conflict and engender further violence in conflict-prone countries: (1) the exacerbating effect of COVID-19 on the underlying root causes of conflict and (2) the exploitation of the crisis by governments and non-state actors who have used the coronavirus to gain political advantage and territorial control. The article uses data collected in real-time by the Armed Conflict Location & Event Data Project (ACLED) and the Johns Hopkins University to illustrate the unfolding and spatial distribution of conflict events before and during the pandemic and combine this with three brief case studies of Afghanistan, Nigeria, and Libya. Descriptive evidence shows how levels of violence have remained unabated or even escalated during the first five months of the pandemic and how COVID-19-related social unrest has spread beyond conflict-affected countries.

scholarly journals Health Workforce Development in Health Emergency and Disaster Risk Management: The Need for Evidence-Based Recommendations

2021 ◽  
Vol 18 (7) ◽  
pp. 3382
Author(s):  
Kevin K. C. Hung ◽  
Sonoe Mashino ◽  
Emily Y. Y. Chan ◽  
Makiko K. MacDermot ◽  
Satchit Balsari ◽  
...  
Keyword(s):  
Risk Management ◽  
Risk Reduction ◽  
Case Studies ◽  
Workforce Development ◽  
Health Workforce ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
Evidence Based ◽  
Literature Reviews

The Sendai Framework for Disaster Risk Reduction 2015–2030 placed human health at the centre of disaster risk reduction, calling for the global community to enhance local and national health emergency and disaster risk management (Health EDRM). The Health EDRM Framework, published in 2019, describes the functions required for comprehensive disaster risk management across prevention, preparedness, readiness, response, and recovery to improve the resilience and health security of communities, countries, and health systems. Evidence-based Health EDRM workforce development is vital. However, there are still significant gaps in the evidence identifying common competencies for training and education programmes, and the clarification of strategies for workforce retention, motivation, deployment, and coordination. Initiated in June 2020, this project includes literature reviews, case studies, and an expert consensus (modified Delphi) study. Literature reviews in English, Japanese, and Chinese aim to identify research gaps and explore core competencies for Health EDRM workforce training. Thirteen Health EDRM related case studies from six WHO regions will illustrate best practices (and pitfalls) and inform the consensus study. Consensus will be sought from global experts in emergency and disaster medicine, nursing, public health and related disciplines. Recommendations for developing effective health workforce strategies for low- and middle-income countries and high-income countries will then be disseminated.

Fiscal stabilisation in real time: an exercise in risk management

2021 ◽  
Author(s):  
Martin Larch ◽  
Diederik Kumps ◽  
Alessandro Cugnasca
Keyword(s):  
Risk Management ◽  
Real Time

Optimising building performance through integrating risk management and building information modelling during the design process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ayman Ahmed Ezzat Othman ◽  
Fatma Othman Alamoudy
Keyword(s):  
Risk Management ◽  
Literature Review ◽  
Case Studies ◽  
Design Process ◽  
Building Performance ◽  
Building Information Modelling ◽  
Content Type ◽  
Information Modelling ◽  
Building Information

Purpose This paper aims to develop a framework for optimising building performance through the integration between risk management (RM) and building information modelling (BIM) during the design process. Design/methodology/approach To achieve this aim, a research strategy consisting of literature review, case studies and survey questionnaire is designed to accomplish four objectives. First, to examine the concepts of design process, building performance, RM and BIM; second, to present three case studies to explain the role of using RM and BIM capabilities towards optimising building performance; third, to investigate the perception and application of architectural design firms in Egypt towards the role of RM and BIM for enhancing building performance during the design process; and finally, to develop a framework integrating RM and BIM during the design process as an approach for optimising building performance. Findings Through literature review, the research identified 18 risks that hamper optimising building performance during the design process. In addition, 11 building performance values and 20 BIM technologies were defined. Results of data analysis showed that “Design budget overrun”, “Lack of considering life cycle cost” and “Inefficient use of the design time” were ranked the highest risks that affect the optimisation of building performance. Respondents ranked “Risk avoid” or “Risk transfer” as the most risk responses adopted in the Egyptian context. In addition, “BIM As Built” was ranked the highest BIM technology used for overcoming risks during the design process. These findings necessitated taking action towards developing a framework to optimising building performance. Originality/value The research identified the risks that affect optimising building performance during the design process. It focuses on improving the design process through using the capabilities of BIM technologies towards overcoming these risks during the design process. The proposed framework which integrates RM and BIM represents a synthesis that is novel and creative in thought and adds value to the knowledge in a manner that has not previously occurred.

IIOT and Real Time Data Analytics - Maximizing the Impact on Safety and Productivity

2021 ◽  
Author(s):  
Ryan Daher ◽  
Nesma Aldash
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Data Analytics ◽  
Contact Tracing ◽  
Time Data ◽  
Seamless Integration ◽  
Location Services ◽  
Industrial Internet ◽  
Potential Applications ◽  
The Impact

Abstract With the global push towards Industry 4.0, a number of leading companies and organizations have invested heavily in Industrial Internet of Things (IIOT's) and acquired a massive amount of data. But data without proper analysis that converts it into actionable insights is just more information. With the advancement of Data analytics, machine learning, artificial intelligence, numerous methods can be used to better extract value out of the amassed data from various IIOTs and leverage the analysis to better make decisions impacting efficiency, productivity, optimization and safety. This paper focuses on two case studies- one from upstream and one from downstream using RTLS (Real Time Location Services). Two types of challenges were present: the first one being the identification of the location of all personnel on site in case of emergency and ensuring that all have mustered in a timely fashion hence reducing the time to muster and lessening the risks of Leaving someone behind. The second challenge being the identification of personnel and various contractors, the time they entered in productive or nonproductive areas and time it took to complete various tasks within their crafts while on the job hence accounting for efficiency, productivity and cost reduction. In both case studies, advanced analytics were used, and data collection issues were encountered highlighting the need for further and seamless integration between data, analytics and intelligence is needed. Achievements from both cases were visible increase in productivity and efficiency along with the heightened safety awareness hence lowering the overall risk and liability of the operation. Novel/Additive Information: The results presented from both studies have highlighted other potential applications of the IIOT and its related analytics. Pertinent to COVID-19, new application of such approach was tested in contact tracing identifying workers who could have tested positive and tracing back to personnel that have been in close proximity and contact therefore reducing the spread of COVID. Other application of the IIOT and its related analytics has also been tested in crane, forklift and heavy machinery proximity alert reducing the risk of accidents.

scholarly journals Case Studies in the Application of an Effective Anticollision Risk Management Standard

2010 ◽  
Author(s):  
Benny Poedjono ◽  
Carlos Manuel Avila ◽  
Phan Van Chinh ◽  
Erhan Isevcan ◽  
John Richard Walker ◽  
...  
Keyword(s):  
Risk Management ◽  
Case Studies ◽  
Management Standard

scholarly journals Improving the FLORIS wind plant model for compatibility with gradient-based optimization

2017 ◽  
Vol 41 (5) ◽  
pp. 313-329 ◽  
Author(s):  
Jared J Thomas ◽  
Pieter MO Gebraad ◽  
Andrew Ning
Keyword(s):  
Steady State ◽  
Wind Turbine ◽  
Case Studies ◽  
Wind Farm ◽  
Optimization Problems ◽  
Algorithmic Differentiation ◽  
Function Calls ◽  
Gradient Based ◽  
Wake Model ◽  
Yaw Angle

The FLORIS (FLOw Redirection and Induction in Steady-state) model, a parametric wind turbine wake model that predicts steady-state wake characteristics based on wind turbine position and yaw angle, was developed for optimization of control settings and turbine locations. This article provides details on changes made to the FLORIS model to make the model more suitable for gradient-based optimization. Changes to the FLORIS model were made to remove discontinuities and add curvature to regions of non-physical zero gradient. Exact gradients for the FLORIS model were obtained using algorithmic differentiation. A set of three case studies demonstrate that using exact gradients with gradient-based optimization reduces the number of function calls by several orders of magnitude. The case studies also show that adding curvature improves convergence behavior, allowing gradient-based optimization algorithms used with the FLORIS model to more reliably find better solutions to wind farm optimization problems.

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