Crisis communication and social media : the changing environment for natural disaster response

Over the past two years there have been several large-scale disasters (Haitian earthquake, Australian floods, UK riots, and the Japanese earthquake) that have seen wide use of social media for disaster response, often in innovative ways. This paper provides an analysis of the ways in which social media has been used in public-to-public communication and public-to-government organisation communication. It discusses four ways in which disaster response has been changed by social media: 1. Social media appears to be displacing the traditional media as a means of communication with the public during a crisis. In particular social media influences the way traditional media communication is received and distributed. 2. We propose that user-generated content may provide a new source of information for emergency management agencies during a disaster, but there is uncertainty with regards to the reliability and usefulness of this information. 3. There are also indications that social media provides a means for the public to self-organise in ways that were not previously possible. However, the type and usefulness of self-organisation sometimes works against efforts to mitigate the outcome of the disaster. 4. Social media seems to influence information flow during a disaster. In the past most information flowed in a single direction from government organisation to public, but social media negates this model. The public can diffuse information with ease, but also expect interaction with Government Organisations rather than a simple one-way information flow. These changes have implications for the way government organisations communicate with the public during a disaster. The predominant model for explaining this form of communication, the Crisis and Emergency Risk Communication (CERC), was developed in 2005 before social media achieved widespread popularity. We will present a modified form of the CERC model that integrates social media into the disaster communication cycle, and addresses the ways in which social media has changed communication between the public and government organisations during disasters.

Crisis communication and social media:the changing environment for natural disaster response

Over the past two years there have been several large-scale disasters (Haitian earthquake, Australian floods, UK riots, and the Japanese earthquake) that have seen wide use of social media for disaster response, often in innovative ways. This paper provides an analysis of the ways in which social media has been used in public-to-public communication and public-to-government organisation communication. It discusses four ways in which disaster response has been changed by social media: 1. Social media appears to be displacing the traditional media as a means of communication with the public during a crisis. In particular social media influences the way traditional media communication is received and distributed. 2. We propose that user-generated content may provide a new source of information for emergency management agencies during a disaster, but there is uncertainty with regards to the reliability and usefulness of this information. 3. There are also indications that social media provides a means for the public to self-organise in ways that were not previously possible. However, the type and usefulness of self-organisation sometimes works against efforts to mitigate the outcome of the disaster. 4. Social media seems to influence information flow during a disaster. In the past most information flowed in a single direction from government organisation to public, but social media negates this model. The public can diffuse information with ease, but also expect interaction with Government Organisations rather than a simple one-way information flow. These changes have implications for the way government organisations communicate with the public during a disaster. The predominant model for explaining this form of communication, the Crisis and Emergency Risk Communication (CERC), was developed in 2005 before social media achieved widespread popularity. We will present a modified form of the CERC model that integrates social media into the disaster communication cycle, and addresses the ways in which social media has changed communication between the public and government organisations during disasters.

Cognitive systems associated with the hippocampus of the human brain and their role in behaviour and neurodegenerative disease

Cognitive systems research involves the synthesis of ideas from natural and artificial systems in the analysis, understanding, and design of all intelligent systems. This chapter discusses the cognitive systems associated with the hippocampus (HC) of the human brain and their possible role in behaviour and neurodegenerative disease. The hippocampus (HC) is concerned with the analysis of highly abstract data derived from all sensory systems but its specific role remains controversial. Hence, there have been three major theories concerning its function, viz., the memory theory, the spatial theory, and the behavioral inhibition theory. The memory theory has its origin in the surgical destruction of the HC, which results in severe anterograde and partial retrograde amnesia. The spatial theory has its origin in the observation that neurons in the HC of animals show activity related to their location within the environment. By contrast, the behavioral inhibition theory suggests that the HC acts as a ‘comparator’, i.e., it compares current sensory events with expected or predicted events. If a set of expectations continues to be verified then no alteration of behavior occurs. If, however, a ‘mismatch’ is detected then the HC intervenes by initiating appropriate action by active inhibition of current motor programs and initiation of new data gathering. Understanding the cognitive systems of the hippocampus in humans may aid in the design of intelligent systems involved in spatial mapping, memory, and decision making. In addition, this information may lead to a greater understanding of the course of clinical dementia in the various neurodegenerative diseases in which there is significant damage to the HC.

Pattern generation by dissipative parametric instability

Nonlinear instabilities are responsible for spontaneous pattern formation in a vast number of natural and engineered systems, ranging from biology to galaxy buildup. We propose a new instability mechanism leading to pattern formation in spatially extended nonlinear systems, which is based on a periodic antiphase modulation of spectrally dependent losses arranged in a zigzag way: an effective filtering is imposed at symmetrically located wave numbers k and -k in alternating order. The properties of the dissipative parametric instability differ from the features of both key classical concepts of modulation instabilities, i.e., the Benjamin-Feir instability and the Faraday instabiltyity. We demonstrate how the dissipative parametric instability can lead to the formation of stable patterns in one- and two-dimensional systems. The proposed instability mechanism is generic and can naturally occur or can be implemented in various physical systems.

The usability of semantic search tools:a review

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

Culture and the psychological impacts of natural disasters: Implications for disaster management and disaster mental health

In recent decades, natural disasters have caused extensive losses and damages to human psychological wellbeing, economy, and society. It has been argued that cultural factors such as social values, traditions, and attachment to a location influence communities facing and responding to natural disasters. However, the issue of culture in disaster mental health seems to have received limited attention in policy and practice. This review highlights the importance of cultural background in the assessment of vulnerability to the psychological impacts of disasters, disaster preparedness, and provision of disaster mental health services. In particular, this paper suggests the importance of cultural competence in the planning and delivery of effective disaster mental health services. In order to address the varying circumstances of people with different cultural backgrounds, disaster mental health services must be developed in a culturally sensitive manner. Development of culturally competent disaster mental health services requires significant changes in policy making, administration, and direct service provision

The TAP approach to intensive and extensive connectivity systems

The Thouless-Anderson-Palmer (TAP) approach was originally developed for analysing the Sherrington-Kirkpatrick model in the study of spin glass models and has been employed since then mainly in the context of extensively connected systems whereby each dynamical variable interacts weakly with the others. Recently, we extended this method for handling general intensively connected systems where each variable has only O(1) connections characterised by strong couplings. However, the new formulation looks quite different with respect to existing analyses and it is only natural to question whether it actually reproduces known results for systems of extensive connectivity. In this chapter, we apply our formulation of the TAP approach to an extensively connected system, the Hopfield associative memory model, showing that it produces identical results to those obtained by the conventional formulation.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.

Environmental scanning electron microscope imaging of vesicle systems

The structural characteristics of liposomes have been widely investigated and there is certainly a strong understanding of their morphological characteristics. Imaging of these systems, using techniques such as freeze-fracturing methods, transmission electron microscopy, and cryo-electron imaging, has allowed us to appreciate their bilayer structures and factors that influence this. However, there are a few methods that study these systems in their natural hydrated state; commonly, the liposomes are visualized after drying, staining and/or fixation of the vesicles. Environmental scanning electron microscopy (ESEM) offers the ability to image a liposome in its hydrated state without the need for prior sample preparation. We were the first to use ESEM to study the liposomes and niosomes, and have been able to dynamically follow the hydration of lipid films and changes in liposome suspensions as water condenses onto, or evaporates from, the sample in real-time. This provides an insight into the resistance of liposomes to coalescence during dehydration, thereby providing an alternative assay for liposome formulation and stability.

Biocomposites of non-crosslinked natural and synthetic polymers

Biocomposite films comprising a non-crosslinked, natural polymer (collagen) and a synthetic polymer, poly(var epsilon-caprolactone) (PCL), have been produced by impregnation of lyophilised collagen mats with a solution of PCL in dichloromethane followed by solvent evaporation. This approach avoids the toxicity problems associated with chemical crosslinking. Distinct changes in film morphology, from continuous surface coating to open porous format, were achieved by variation of processing parameters such as collagen:PCL ratio and the weight of the starting lyophilised collagen mat. Collagenase digestion indicated that the collagen content of 1:4 and 1:8 collagen:PCL biocomposites was almost totally accessible for enzymatic digestion indicating a high degree of collagen exposure for interaction with other ECM proteins or cells contacting the biomaterial surface. Much reduced collagen exposure (around 50%) was measured for the 1:20 collagen:PCL materials. These findings were consistent with the SEM examination of collagen:PCL biocomposites which revealed a highly porous morphology for the 1:4 and 1:8 blends but virtually complete coverage of the collagen component by PCL in the1:20 samples. Investigations of the attachment and spreading characteristics of human osteoblast (HOB) cells on PCL films and collagen:PCL materials respectively, indicated that HOB cells poorly recognised PCL but attachment and spreading were much improved on the biocomposites. The non-chemically crosslinked, collagen:PCL biocomposites described are expected to provide a useful addition to the range of biomaterials and matrix systems for tissue engineering.

A structural method for the design of fault tolerant distributed control systems

Distributed digital control systems provide alternatives to conventional, centralised digital control systems. Typically, a modern distributed control system will comprise a multi-processor or network of processors, a communications network, an associated set of sensors and actuators, and the systems and applications software. This thesis addresses the problem of how to design robust decentralised control systems, such as those used to control event-driven, real-time processes in time-critical environments. Emphasis is placed on studying the dynamical behaviour of a system and identifying ways of partitioning the system so that it may be controlled in a distributed manner. A structural partitioning technique is adopted which makes use of natural physical sub-processes in the system, which are then mapped into the software processes to control the system. However, communications are required between the processes because of the disjoint nature of the distributed (i.e. partitioned) state of the physical system. The structural partitioning technique, and recent developments in the theory of potential controllability and observability of a system, are the basis for the design of controllers. In particular, the method is used to derive a decentralised estimate of the state vector for a continuous-time system. The work is also extended to derive a distributed estimate for a discrete-time system. Emphasis is also given to the role of communications in the distributed control of processes and to the partitioning technique necessary to design distributed and decentralised systems with resilient structures. A method is presented for the systematic identification of necessary communications for distributed control. It is also shwon that the structural partitions can be used directly in the design of software fault tolerant concurrent controllers. In particular, the structural partition can be used to identify the boundary of the conversation which can be used to protect a specific part of the system. In addition, for certain classes of system, the partitions can be used to identify processes which may be dynamically reconfigured in the event of a fault. These methods should be of use in the design of robust distributed systems.

Liposomes:Formulation and characterisation as contrast agents and as vaccine delivery systems

Liposome systems are well reported for their activity as vaccine adjuvants; however novel lipid-based microbubbles have also been reported to enhance the targeting of antigens into dendritic cells (DCs) in cancer immunotherapy (Suzuki et al 2009). This research initially focused on the formulation of gas-filled lipid coated microbubbles and their potential activation of macrophages using in vitro models. Further studies in the thesis concentrated on aqueous-filled liposomes as vaccine delivery systems. Initial work involved formulating and characterising four different methods of producing lipid-coated microbubbles (sometimes referred to as gas-filled liposomes), by homogenisation, sonication, a gas-releasing chemical reaction and agitation/pressurisation in terms of stability and physico-chemical characteristics. Two of the preparations were tested as pressure probes in MRI studies. The first preparation composed of a standard phospholipid (DSPC) filled with air or nitrogen (N2), whilst in the second method the microbubbles were composed of a fluorinated phospholipid (F-GPC) filled with a fluorocarbon saturated gas. The studies showed that whilst maintaining high sensitivity, a novel contrast agent which allows stable MRI measurements of fluid pressure over time, could be produced using lipid-coated microbubbles. The F-GPC microbubbles were found to withstand pressures up to 2.6 bar with minimal damage as opposed to the DSPC microbubbles, which were damaged at above 1.3 bar. However, it was also found that DSPC-filled with N2 microbubbles were also extremely robust to pressure and their performance was similar to that of F-GPC based microbubbles. Following on from the MRI studies, the DSPC-air and N2 filled lipid-based microbubbles were assessed for their potential activation of macrophages using in vitro models and compared to equivalent aqueous-filled liposomes. The microbubble formulations did not stimulate macrophage uptake, so studies thereafter focused on aqueous-filled liposomes. Further studies concentrated on formulating and characterising, both physico-chemically and immunologically, cationic liposomes based on the potent adjuvant dimethyldioctadecylammonium (DDA) and immunomodulatory trehalose dibehenate (TDB) with the addition of polyethylene glycol (PEG). One of the proposed hypotheses for the mechanism behind the immunostimulatory effect obtained with DDA:TDB is the ‘depot effect’ in which the liposomal carrier helps to retain the antigen at the injection site thereby increasing the time of vaccine exposure to the immune cells. The depot effect has been suggested to be primarily due to their cationic nature. Results reported within this thesis demonstrate that higher levels of PEG i.e. 25 % were able to significantly inhibit the formation of a liposome depot at the injection site and also severely limit the retention of antigen at the site. This therefore resulted in a faster drainage of the liposomes from the site of injection. The versatility of cationic liposomes based on DDA:TDB in combination with different immunostimulatory ligands including, polyinosinic-polycytidylic acid (poly (I:C), TLR 3 ligand), and CpG (TLR 9 ligand) either entrapped within the vesicles or adsorbed onto the liposome surface was investigated for immunogenic capacity as vaccine adjuvants. Small unilamellar (SUV) DDA:TDB vesicles (20-100 nm native size) with protein antigen adsorbed to the vesicle surface were the most potent in inducing both T cell (7-fold increase) and antibody (up to 2 log increase) antigen specific responses. The addition of TLR agonists poly(I:C) and CpG to SUV liposomes had small or no effect on their adjuvanticity. Finally, threitol ceramide (ThrCer), a new mmunostimulatory agent, was incorporated into the bilayers of liposomes composed of DDA or DSPC to investigate the uptake of ThrCer, by dendritic cells (DCs), and presentation on CD1d molecules to invariant natural killer T cells. These systems were prepared both as multilamellar vesicles (MLV) and Small unilamellar (SUV). It was demonstrated that the IFN-g secretion was higher for DDA SUV liposome formulation (p<0.05), suggesting that ThrCer encapsulation in this liposome formulation resulted in a higher uptake by DCs.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.