Framework for assessing the influence of critical infrastructure interdependencies in post-disaster reconstruction management

Due to ever increasing climate instability, the number of natural disasters affecting society and communities is expected to increase globally in the future, which will result in a growing number of casualties and damage to property and infrastructure. Such damage poses crucial challenges for recovery of interdependent critical infrastructures. Post-disaster reconstruction is a complex undertaking as it is not only closely linked to the well-being and essential functioning of society, but also requires a large financial commitment. Management of critical infrastructure during post-disaster recovery needs to be underpinned by a holistic recognition that the recovery of each individual infrastructure system (e.g. energy, water, transport and information and communication technology) can be affected by the interdependencies that exist between these different systems. A fundamental characteristic of these interdependencies is that failure of one critical infrastructure system can result in the failure of other interdependent infrastructures, leading to a cascade of failures, which can impede post-disaster recovery and delay the subsequent reconstruction process. Consequently, there is a critical need for developing a holistic strategy to assess the influence of infrastructure interdependencies, and for incorporating these interdependencies into a post-disaster recovery strategy. This paper discusses four key dimensions of interdependencies that need to be considered in a post-disaster reconstruction planning. Using key concepts and sub-concepts derived from the notion of interdependency, the paper examines how critical infrastructure interdependencies affect the recovery processes of damaged infrastructures.

"It broke our hearts": Understanding parents' lived experiences of their child's admission to an acute mental health care facility

This article reports the evaluative findings of an Early Psychosis Education Program (EPEP) designed to support parents caring for their child who was recently admitted to the psychiatric intensive care unit of an inpatient mental health care facility in Australia. The EPEP offered education on mental illness, treatment options, and medication, as well as information on the recovery model of care. The EPEP was facilitated by two RNs and was evaluated for educational effectiveness using a simple pre- and postevaluation questionnaire. The evaluation revealed two themes expressed by parents: "We didn't see it coming," and "Hopelessness and helplessness." The themes highlighted the parents' lack of mental health care knowledge prior to the EPEP, which had a significant impact on the parents' experiences and well-being. The evaluative findings highlighted a need for a nurse-led EPEP within the community. A community EPEP has the potential to strengthen the partnership between parents, families, and mental health service providers and to help with the provision of a recovery framework of care.

Have you got what it takes? Nursing in a Psychiatric Intensive Care Unit

Purpose A Psychiatric Intensive Care Unit (PICU) and or High Dependency Unit (HDU) is a locked, intensive treatment facility available to people experiencing acute psychiatric distress. For many people who access public mental health services in Australia, the PICU/HDU is the primary point of admission, and should represent and facilitate timely assessment and an optimum treatment plan under a recovery framework. Nurses are the largest health discipline working in this specialty area of care. The paper aims to discuss these issues. Design/methodology/approach A qualitative study aimed to investigate the skills, experience, and practice, of nurses working in the PICU/HDU in relation to a recovery model of care. Identifying how nurses provide care in the PICU/HDU will inform a clinical practice guideline to further support this specialty area of care. Four focus groups were facilitated with 52 registered nurses attending. Findings The nurse participants identified specific skills under four distinct themes; Storytelling, Treatment and recovery, Taking responsibility, and Safeguarding. The skills highlight the expertise and clinical standard required to support a recovery model of care in the PICU. Research limitations/implications – The research findings highlight urgency for a National PICU/HDU clinical practice guideline. Practical implications A PICU/HDU practice guideline will promote the standard of nursing care required in the PICU/HDU. The PICU/HDU needs to be recognised as a patient centred, therapeutic opportunity as opposed to a restrictive and custodial clinical area. Social implications Providing transparency of practice in the PICU/HDU and educating nurses to this specialty area of care will improve client outcome and recovery. Originality/value Very few studies have explored the skills, experience, and practice, of nurses working in the PICU/HDU in relation to a recovery model of care. A dearth of research exists on what is required to work in this specialty area of care.

Assessment of strategies for linking the damage and loss assessment methodology to the post-disaster needs assessment

This report was prepared at the request of the United Nations Economic Commission for Latin America and the Caribbean (ECLAC) with support from the Caribbean Catastrophe Risk Insurance Facility (CCRIF) to assess strategies for linking the ECLAC Damage and Loss Assessment (DaLA) Methology to the Post Disaster Needs Assessment (PDNA). Each metholodolgy was individually outlined and their use in the Caribbean context was explored in detail to set the framework or lens through which their linking would be viewed. Other methologies that are used within the recovery process were identified and outlined. A gap analysis was conducted on moving from the PDNA with a focus on initial rapid reponse to DaLA. DaLA training materials were reviewed to assess where improvements can be made to seamlessly move from one methology to the next. Additionally, both DaLA and PDNA reports were reviewed to identify specific areas of information which could serve as common data links, and note how this linkage could inform the overall disaster assessments in the region. This is in addition to noting any similarities or variance in the application of both methologies. Challenges to linking both methodologies were identified such as countries lacking well defined recovery frameworks and their ability to fund or finance recovery efforts, in addition to recurrent challenges in the Caribbean region such as inadequacy of baseline data, human resource and training, and identifying teams to conduct the data collection. Recommendations made in terms of the strategies to be employed for the successful linking of both the DaLA and PDNA Methodologies included: creating and maintaining a recovery framework and baseline data; creation of a minimum requirements list for the successful implementation of PDNA and DaLA implementation; and increasing political will in addition to identify a champion to push the subject.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

Joint pitch-analysis formant-synthesis framework for CS recovery of speech

A joint analysis-synthesis framework is developed for the compressive sensing (CS) recovery of speech signals. The signal is assumed to be sparse in the residual domain with the linear prediction filter used as the sparse transformation. Importantly this transform is not known apriori, since estimating the predictor filter requires the knowledge of the signal. Two prediction filters, one comb filter for pitch and another all pole formant filter are needed to induce maximum sparsity. An iterative method is proposed for the estimation of both the prediction filters and the signal itself. Formant prediction filter is used as the synthesis transform, while the pitch filter is used to model the periodicity in the residual excitation signal, in the analysis mode. Significant improvement in the LLR measure is seen over the previously reported formant filter estimation.

A spatio-temporal 2D-models framework for human pose recovery in monocular sequences

This paper addresses the pose recovery problem of a particular articulated object: the human body. In this model-based approach, the 2D-shape is associated to the corresponding stick figure allowing the joint segmentation and pose recovery of the subject observed in the scene. The main disadvantage of 2D-models is their restriction to the viewpoint. To cope with this limitation, local spatio-temporal 2D-models corresponding to many views of the same sequences are trained, concatenated and sorted in a global framework. Temporal and spatial constraints are then considered to build the probabilistic transition matrix (PTM) that gives a frame to frame estimation of the most probable local models to use during the fitting procedure, thus limiting the feature space. This approach takes advantage of 3D information avoiding the use of a complex 3D human model. The experiments carried out on both indoor and outdoor sequences have demonstrated the ability of this approach to adequately segment pedestrians and estimate their poses independently of the direction of motion during the sequence. (c) 2008 Elsevier Ltd. All rights reserved.

A framework for monitoring and runtime recovery of web service-based applications

Service provisioning is a challenging research area for the design and implementation of autonomic service-oriented software systems. It includes automated QoS management for such systems and their applications. Monitoring, Diagnosis and Repair are three key features of QoS management. This work presents a self-healing Web service-based framework that manages QoS degradation at runtime. Our approach is based on proxies. Proxies act on meta-level communications and extend the HTTP envelope of the exchanged messages with QoS-related parameter values. QoS Data are filtered over time and analysed using statistical functions and the Hidden Markov Model. Detected QoS degradations are handled with proxies. We experienced our framework using an orchestrated electronic shop application (FoodShop).

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

An Investigation of Cognitive, Emotional and Negative Behavioural Responses to Service Recovery Strategies

Despite the best intentions of service providers and organisations, service delivery is rarely error-free. While numerous studies have investigated specific cognitive, emotional or behavioural responses to service failure and recovery, these studies do not fully capture the complexity of the services encounter. Consequently, this research develops a more holistic understanding of how specific service recovery strategies affect the responses of customers by combining two existing models—Smith & Bolton’s (2002) model of emotional responses to service performance and Fullerton and Punj’s (1993) structural model of aberrant consumer behaviour—into a conceptual framework. Specific service recovery strategies are proposed to influence consumer cognition, emotion and behaviour. This research was conducted using a 2x2 between-subjects quasi-experimental design that was administered via written survey. The experimental design manipulated two levels of two specific service recovery strategies: compensation and apology. The effect of the four recovery strategies were investigated by collecting data from 18-25 year olds and were analysed using multivariate analysis of covariance and multiple regression analysis. The results suggest that different service recovery strategies are associated with varying scores of satisfaction, perceived distributive justice, positive emotions, negative emotions and negative functional behaviour, but not dysfunctional behaviour. These finding have significant implications for the theory and practice of managing service recovery.

Comparing the diffusion of online service recovery in small and large organisations

A key concern organisations face is how to incorporate Internet tools into their marketing communications mix. Where and how should companies invest their human, technological and financial resources? This paper explores a subset of this problem, online complaining and electronic customer service. It applies diffusion of innovation as a theoretical framework to investigate organisational implementation of email technology and explain the outcome of annual customer service surveys in 2001, 2002 and 2003. The results add to the small body of research on electronic service recovery by extending diffusion of innovations to email service recovery and underscoring the importance of adoption phases, particularly for SMEs. Larger companies provide more channels for submitting complaints, which represents an early phase of adoption. There was little difference in how large and small companies respond to online complaints, a later phase of adoption.

Redefining life cycle for a building sustainability assessment framework

Understanding the differences between the temporal and physical aspects of the building life cycle is an essential ingredient in the development of Building Environmental Assessment (BEA) tools. This paper illustrates a theoretical Life Cycle Assessment (LCA) framework aligning temporal decision-making with that of material flows over building development phases. It was derived during development of a prototype commercial building design tool that was based on a 3-D CAD information and communications technology (ICT) platform and LCA software. The framework aligns stakeholder BEA needs and the decision-making process against characteristics of leading green building tools. The paper explores related integration of BEA tool development applications on such ICT platforms. Key framework modules are depicted and practical examples for BEA are provided for: • Definition of investment and service goals at project initiation; • Design integrated to avoid overlaps/confusion over the project life cycle; • Detailing the supply chain considering building life cycle impacts; • Delivery of quality metrics for occupancy post-construction/handover; • Deconstruction profiling at end of life to facilitate recovery.