Resilience Uncovered: A review of professional resilience measurement methodologies

Recent policy developments on the global, regional and national levels have materialised strategic goals for building resilience. Implementing resilience fundamentally requires operationalising the concept in order to make it measurable. This thesis scoped for professional resilience measurement methodologies across grey literature and analysed their organisational purposes and specifics of measurement indicators in the light of cohesion and collaborative potential. 55 methodologies from 52 organisations were found. It was concluded that resilience measurements are mainly used for strategic programming and monitoring and evaluation purposes. Overall, the methodologies clearly delineated to six sectoral groups; development/humanitarian, safety/disaster risk management, critical infrastructure/utilities, social welfare, economic and environmental. All of the sectoral groups were conceptually cohesive among their resilience operationalisations. Cross-sectoral cooperation was estimated based on the rates at which disaster risk management, climate change adaptation and complexity were integrated within each sector. Development/humanitarian and safety/disaster risk management sectors both integrated climate change adaptation with a high prevalence. On the other hand, disaster risk management was integrated by safety/disaster risk management and critical infrastructure/utilities sectors with a high prevalence. When it comes to the measurement designs, it was noted that higher scale measurements were more prominent in using secondary data sets than lower scale measurements. Furthermore, it was observed that qualitative measurements were somewhat more common (52.8% of all methodologies) than quantitative measurements (43.8%). The research aim was fulfilled by establishing scientific knowledge on how resilience is operationalised by professional organisations. Based on the resilience operationalisations, resilience seems to be used in a somewhat isolated manner among sectors. While in-sector conceptual cohesion exists, the outcome goals and used concepts vary between sectors. When it comes to inter-organisational cohesion, it was concluded that conceptual heterogeneity exists among most of the identified sectors.Those who have faced and discussed resilience in their work in academics or elsewhere know that the concept is difficult to penetrate and apply. Many have noted that resilience’s multi-faceted nature is largely impacting its practical use. To provide contextual understanding about this issue, the thesis focused on identifying professional resilience measurement methodologies, deriving and analysing their organisational purposes, conceptualisations of resilience and measurement practicalities. The idea was that discussing resilience and implementing resilience are fundamentally two different approaches, the latter of which provides pragmatic knowledge about resilience’s organisational relevance. Overall, 55 methodologies were identified from 52 organisations. Interestingly, the most prominent organisational purposes were related to project programming (65.5%) and their monitoring and evaluation (25.5%), both mutually non-exclusive. When it comes to the sectors that utilise resilience measurements, the derived organisational sectors were development/humanitarian (43.6%), safety/disaster risk management (21.8%), critical infrastructure/utilities (14.5%), social welfare (7.3%), economic (7.3%) and environmental (5.5%) sectors. Overall, qualitative and quantitative measurements were both present at 52.8% and 43.8% prevalence, respectively. In practical terms, several observations with critical value were made. It was noted that resilience definitions were largely ignored in the resilience measurement operationalisations. Hence, collaborative negations for projects involving resilience should always include discussions determining how the concept is operationalised and measured in the project. Furthermore, the thesis determined that resilience seems to be used in a somewhat isolated manner between the identified organisational sectors. Each sector utilised ‘sector-specific’ resilience outcome goals and conceptual operationalisations of resilience, which establish in-sector conceptual cohesion, but at the same differentiate each sector from another. For future research, the thesis suggests that studying how resilience is used inside organisations, and more specifically determining what type of information roles it fulfills, can help to link the research of resilience to the research of organisational change. Moreover, studying how the implementation of resilience affects network dynamics, especially in knowledge networks such the humanitarian cluster system, can help to record lessons learned to support the future implementation of resilience frameworks elsewhere

Subsidiarity, judicial review and national parliaments after Lisbon : theory and practice

Subsidiarity: Theory and Practice before the CJEUPeer reviewe

Context and communication profiling for IoT security and privacy: techniques and applications

During the last decade, two major technological changes have profoundly changed the way in which users consume and interact with on-line services and applications. The first of these has been the success of mobile computing, in particular that of smartphones, the primary end device used by many users for access to the Internet and various applications. The other change is the emergence of the so-called Internet-of-Things (IoT), denoting a technological transition in which everyday objects like household appliances that traditionally have been seen as stand-alone devices, are given network connectivity by introducing digital communication capabilities to those devices. The topic of this dissertation is related to a core challenge that the emergence of these technologies is introducing: how to effectively manage the security and privacy settings of users and devices in a user-friendly manner in an environment in which an ever-growing number of heterogeneous devices live and co-exist with each other? In particular we study approaches for utilising profiling of contextual parameters and device communications in order to make autonomous security decisions with the goal of striking a better balance between a system's security on one hand, and, its usability on the other. We introduce four distinct novel approaches utilising profiling for this end. First, we introduce ConXsense, a system demonstrating the use of user-specific longitudinal profiling of contextual information for modelling the usage context of mobile computing devices. Based on this ConXsense can probabilistically automate security policy decisions affecting security settings of the device. Further we develop an approach utilising the similarity of contextual parameters observed with on-board sensors of co-located devices to construct proofs of presence that are resilient to context-guessing attacks by adversaries that seek to fool a device into believing the adversary is co-located with it, even though it is in reality not. We then extend this approach to a context-based key evolution approach that allows IoT devices that are co-present in the same physical environment like the same room to use passively observed context measurements to iteratively authenticate their co-presence and thus gradually establish confidence in the other device being part of the same trust domain, e.g., the set of IoT devices in a user's home. We further analyse the relevant constraints that need to be taken into account to ensure security and usability of context-based authentication. In the final part of this dissertation we extend the profiling approach to network communications of IoT devices and utilise it to realise the design of the IoTSentinel system for autonomous security policy adaptation in IoT device networks. We show that by monitoring the inherent network traffic of IoT devices during their initial set-up, we can automatically identify the type of device newly added to the network. The device-type information is then used by IoTSentinel to adapt traffic filtering rules automatically to provide isolation of devices that are potentially vulnerable to known attacks, thereby protecting the device itself and the rest of the network from threats arising from possible compromise of vulnerable devices

Computational modeling of cationic lipid bilayers in saline solutions

Based on computer simulations performed at single-molecule resolution, the effects of monovalent NaCl salt on cationic DMTAP/DMPC (dimyristoyltrimethylammoniumpropane/dimyristoylphosphatidylcholine) lipid bilayer systems are discussed. The monograph reviews, revises and expands the previously published work on how NaCl affects the structural and electrostatic [1] and the dynamic [2] properties of these systems. The effects of NaCl depended qualitatively on the cationic DMTAP lipid fraction. When the fraction was low, NaCl had a notable effect of the structural properties of the bilayer, decreasing the area per lipid, increasing the tail order, reorienting the DMPC head groups, and increasing the average electrostatic potential difference over the head group region. At high DMTAP fraction there was scarcely an effect when NaCl was added. The reason for this dichotomy was the ability of the Na+ ions to bind with the DMPC lipid carbonyl oxygens at low DMTAP fraction and to tie 2 to 4 DMPCs into a dynamic complex. At high DMTAP fraction the binding of Na+ was prevented by the high positive surface charge of the bilayer. The lateral diffusion of Na+ ions within the carbonyl region had two qualitatively different modes. Na+ ions bound to a DMPC diffused very slowly, whereas the free Na+ ions traveled rapidly within the carbonyl region. The combined effect of the two motions appeared as Na+ ions hopping from one DMPC carbonyl oxygen to the next

IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IOT SENTINEL is effective in identifying device types and has minimal performance overhead

Probing the elastic response of lipid bilayers and nanovesicles to leaflet tensions via volume per lipid

Biological and biomimetic membranes are based on lipid bilayers, consisting of two monolayers or leaflets. One important but challenging physical parameter of these membranes is their tension. For a long time, this tension was explicitly or implicitly taken to be the bilayer tension, acting on the whole bilayer membrane. More recently, it has been realized that it is useful to decompose the bilayer tension into two leaflet tensions and that these tensions are accessible to molecular dynamics simulations. To divide the bilayer up into two leaflets, it is necessary to introduce a midsurface that defines the spatial extent of the two leaflets. In previous studies, this midsurface was obtained from the density profiles across the bilayer and was then used to compute the molecular area per lipid. Here, we develop an alternative approach based on three-dimensional Voronoi tessellation and molecular volume per lipid. Using this volume-based approach, we determine the reference states with tensionless leaflets as well as the optimal volumes and areas per lipid. The optimal lipid volumes have practically the same value in both leaflets, irrespective of the size and curvature of the nanovesicles, whereas the optimal lipid areas are different for the two leaflets and depend on the vesicle size. In addition, we introduce lateral volume compressibilities to describe the elastic response of the lipid volume to the leaflet tensions. We show that the outer leaflet of a nanovesicle is more densely packed and less compressible than the inner leaflet and that this difference becomes more pronounced for smaller vesicles.publishedVersio

Shorter birth intervals between siblings are associated with increased risk of parental divorce

Birth intervals are a crucial component of fertility behaviour and family planning. Short birth intervals are associated—although not necessarily causally—with negative health-related outcomes, but less is known about their associations with family functioning. Here, the associations between birth intervals and marital stability were investigated by Cox regression using a nationally representative, register-based sample of individuals with two (N = 42,481) or three (N = 22,514) children from contemporary Finland (observation period 1972–2009). Shorter interbirth intervals were associated with an increased risk of parental divorce over a ten-year follow-up. Individuals with birth intervals of up to 1.5 years had 24–49 per cent higher divorce risk compared to individuals whose children were born more than 4 years apart. The pattern was similar in all socioeconomic groups and among individuals with earlier and later entry to parenthood. Our results add to the growing body of research showing associations between short birth intervals and negative outcomes in health and family functioning.Peer reviewe

Dimension Reduction for Time Series in a Blind Source Separation Context Using R

Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first reducing the dimension of the series and then modelling the resulting uncorrelated signals univariately, avoiding the need for any covariance parameters. A popular and effective framework for this is blind source separation. In this paper we review the dimension reduction tools for time series available in the R package tsBSS. These include methods for estimating the signal dimension of second-order stationary time series, dimension reduction techniques for stochastic volatility models and supervised dimension reduction tools for time series regression. Several examples are provided to illustrate the functionality of the package