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Key elements of disaster management are prevention, mitigation, preparedness, response and relief, rehabilitation. The various stakeholders in the process of disaster mitigation are policy makers, decision makers, administration, professionals, professional institutions, R&D institutions, financial institutions, insurance sector, community, NGOs and the common man. Insurance has played a very important role. The advanced countries have developed the insurance system and made it effective and mandatory – as a result the loss of lives and property is comparatively less. In India, most of the losses suffered in natural disasters are not insured, for reasons such as lack of purchasing power, lack of interest in insurance, theory of karma attitude and ignorance of availability of such covers. Quite large numbers of agencies provide the insurance cover and foreign insurance companies have already ventured in such areas. This implies that the commercial and private sector can also play an essential role in disaster mitigation. The present study attempts to fill the gap in studies on the role of the insurance sector in disaster management.

The purpose of the article is to develop an equation to accurately represent OCV as a function of SoC with reduced computational burden. Dependency of open circuit voltage (OCV) on state of charge (SoC) is often represented by either a look-up table or an equation developed by regression analysis. The accuracy is increased by either a larger data set for the look-up table or using a higher order equation for the regression analysis. Both of them increase the memory requirement in the controller. In this paper, Gaussian exponential regression methodology is proposed to represent OCV and SoC relationships accurately, with reduced memory requirement.

Incremental OCV test under constant temperature provides a data set of OCV and SoC. This data set is subjected to polynomial, Gaussian and the proposed Gaussian exponential equations. The unknown coefficients of these equations are obtained by least residual algorithm and differential evolution–based fitting algorithms for charging, discharging and average OCV.

Root mean square error (RMSE) of the proposed equation for differential evolution–based fitting technique is 35% less than second-order Gaussian and 74% less than a fifth-order polynomial equation for average OCV with a 16.66% reduction in number of coefficients, thereby reducing memory requirement.

The knee structure in the OCV and SoC relationship is accurately represented by Gaussian first-order equation, and the exponential equation can accurately describe the linear relation. Therefore, this paper proposes a Gaussian exponential equation that accurately represents the OCV as a function of SoC. The results obtained from the proposed regression methodology are compared with the polynomial and Gaussian regression in terms of RMSE, mean average, variance and number of coefficients.

United Arab Emirates (UAE) has experienced various natural and man-made hazards. The energy sector dominates in the UAE and protection of this critical infrastructure against any impending hazards is important. The purpose of this paper is to examine the vulnerability of Emirati energy sector, explored barriers to enhance resilience and ways to overcome identified barriers.

Study followed case study research strategy and multiple case studies; three electricity generating power plant facilities – one in Abu Dhabi, one in Dubai and one in Sharjah were selected. Data collection involved a combination of a questionnaire survey and semi-structured face-to-face interviews. In total 42 questionnaire responses and nine interviews among Emirati energy sector employees were analyzed.

Results indicated terrorism, atmospheric and tectonic hazards as the three main risks of vulnerability within Emirati energy sector; whereas “lack of or absence of national government legislation” and “awareness and education” are revealed as the main barriers. Improving human resource management within Emirati energy sector through better awareness, training and practices is considered as a priority.

It is argued that the vulnerabilities and barriers identified in this study can be applicable to Emirati energy sector in general, not just the case studies represented in the study.

The feedback from the members of the Emirati energy sector is indicative of the latent failures and urgent issues that need to be addressed; predominately those of training, education and awareness. Findings of this study may help UAE energy sector to be prepared and build resilience for future hazards.

Knowledge management can play a vital role through ensuring the availability and accessibility of accurate and reliable disaster risk information when required and through effective lesson learning. Identification of key disaster knowledge factors will be an enabler to manage disasters successfully. The purpose of this paper is to present key knowledge factors relating to disaster management cycle, and explore a few challenges relating to identified disaster knowledge factors.

A list of disaster knowledge factors was first identified through a comprehensive literature review and later semi‐structured interviews were conducted among few disaster management practitioners to explore the influence and challenges relating to identified knowledge factors.

Technological, operational/managerial, economic, social, legal and environmental factors seem to have direct influence over the disaster management cycle, while the influence of institutional and political factors seemed indirect and it is through other factors identified. Among key challenges, the lack of detection and warning systems, the need for effective education, training and awareness raising programmes, the need for regular updating of disaster related laws, lack of funds for economic planning measures, poor planning, poor communication, poor leadership, and poor institutional arrangement were highlighted.

Owing to paucity of literature and inadequate empirical research done, this paper provides the basis for more empirical research on disaster knowledge factors and sharing of lessons learned. In order to enhance the management of disasters in future, challenges identified need to be addressed.

This paper aims to explore the issues of information technology (IT) disaster recovery (DR) and business continuity planning (BCP) in light of Cyclone Gonu in Oman.

The paper includes a survey of the latest literature on the subject, then documents a study of public and private sector organizations together with their DR and BCP practices.

The paper investigates how public and private organizations in Oman plan to respond to disasters. It shows that while some organizations pay attention to the need for DR/BCP, many do not. A significant finding is that while organizations have disaster related plans, almost half of those surveyed do not rehearse them. Nevertheless, organizations surveyed indicate that they have learned valuable lessons from Gonu. It remains to be seen whether these lessons will be turned into effective and properly deployed DR/BCP plans.

This paper draws lessons from the experiences and challenges raised by Gonu, and concludes with a set of recommendations that organizations may adopt to ensure business continuity. It provides a useful evaluation of the preparedness of IT departments in both public and private sectors in Oman. The recommendations given at the end of the paper could be of a great value for many organizations and groups, spreading awareness of the importance of being prepared for such eventualities.

Motivated by the problems that the positioning error of strap-down inertial navigation system (SINS) accumulates over time and few sensors are available for midwater navigation, this paper aims to propose a self-aided SINS scheme for the spiral-diving human-occupied vehicle (HOV) based on the characteristics of maneuvering pattern and SINS error propagation.

First, the navigation equations of SINS are simultaneously executed twice with the same inertial measurement unit (IMU) data as input to obtain two sets of SINS. Then, to deal with the horizontal velocity provided by one SINS, a delay-correction high-pass filter without phase shift and amplitude attenuation is designed. Finally, the horizontal velocity after processing is used to integrate with other SINS.

Simulation results indicate that the horizontal positioning error of the proposed scheme is less than 0.1 m when an HOV executes spiral diving to 7,000 meters under the sea and it is inherently able to estimate significant sensors biases.

The proposed scheme can provide a precise navigation solution without error growth for spiral-diving HOV on the condition that only IMU is required as a navigation sensor.