Improving resilience in Critical Infrastructures through learning from past events

Modern societies are increasingly dependent on the proper functioning of Critical Infrastructures (CIs). CIs produce and distribute essential goods or services, as for power transmission systems, water treatment and distribution infrastructures, transportation systems, communication networks, nuclear power plants, and information technologies. Being resilient, where resilience denotes the capacity of a system to recover from challenges or disruptive events, becomes a key property for CIs, which are constantly exposed to threats that can undermine safety, security, and business continuity. Nowadays, a variety of approaches exists in the context of CIs’ resilience research. This dissertation starts with a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the approaches that have a complete qualitative dimension, or that can be used as entry points for semi-quantitative analyses. The review identifies four principal dimensions of resilience referred to CIs (i.e., techno-centric, organizational, community, and urban) and discusses the related qualitative or semi-quantitative methods. The scope of the thesis emphasizes the organizational dimension, as a socio-technical construct. Accordingly, the following research question has been posed: how can learning improve resilience in an organization? Firstly, the benefits of learning in a particular CI, i.e. the supply chain in reverse logistics related to the small arms utilized by Italian Armed Forces, have been studied. Following the theory of Learning From Incidents, the theoretical model helped to elaborate a centralized information management system for the Supply Chain Management of small arms within a Business Intelligence (BI) framework, which can be the basis for an effective decision-making process, capable of increasing the systemic resilience of the supply chain itself. Secondly, the research question has been extended to another extremely topical context, i.e. the Emergency Management (EM), exploring the crisis induced learning where single-loop and double-loop learning cycles can be established regarding the behavioral perspective. Specifically, the former refers to the correction of practices within organizational plans without changing core beliefs and fundamental rules of the organization, while the latter aims at resolving incompatible organizational behavior by restructuring the norms themselves together with the associated practices or assumptions. Consequently, with the aim of ensuring high EM systems resilience, and effective single-loop and double-loop crisis induced learning at organizational level, the study examined learning opportunities that emerge through the exploration of adaptive practices necessary to face the complexity of a socio-technical work domain as the EM of Covid-19 outbreaks on Oil & Gas platforms. Both qualitative and quantitative approaches have been adopted to analyze the resilience of this specific socio-technical system. On this consciousness, with the intention to explore systems theoretic possibilities to model the EM system, the Functional Resonance Analysis Method (FRAM) has been proposed as a qualitative method for developing a systematic understanding of adaptive practices, modelling planning and resilient behaviors and ultimately supporting crisis induced learning. After the FRAM analysis, the same EM system has also been studied adopting a Bayesian Network (BN) to quantify resilience potentials of an EM procedure resulting from the adaptive practices and lessons learned by an EM organization. While the study of CIs is still an open and challenging topic, this dissertation provides methodologies and running examples on how systemic approaches may support data-driven learning to ultimately improve organizational resilience. These results, possibly extended with future research drivers, are expected to support decision-makers in their tactical and operational endeavors

Assessing system architectures: the Canonical Decomposition Fuzzy Comparative methodology

The impacts of decisions made during the selection of the system architecture propagate throughout the entire system lifecycle. The challenge for system architects is to perform a realistic assessment of an inherently ambiguous system concept. Subject matter expert interpretations, intuition, and heuristics are performed quickly and guide system development in the right overall direction, but these methods are subjective and unrepeatable. Traditional analytical assessments dismiss complexity in a system by assuming severability between system components and are intolerant of ambiguity. To be defensible, a suitable methodology must be repeatable, analytically rigorous, and yet tolerant of ambiguity. The hypothesis for this research is that an architecture assessment methodology capable of achieving these objectives is possible by drawing on the strengths of existing approaches while addressing their collective weaknesses. The proposed methodology is the Canonical Decomposition Fuzzy Comparative approach. The theoretical foundations of this methodology are developed and tested through the assessment of three physical architectures for a peer-to-peer wireless network. An extensible modeling framework is established to decompose high-level system attributes into technical performance measures suitable for analysis via computational modeling. Canonical design primitives are used to assess antenna performance in the form of a comparative analysis between the baseline free space gain patterns and the installed gain patterns. Finally, a fuzzy inference system is used to interpret the comparative feature set and offer a numerical assessment. The results of this experiment support the hypothesis that the proposed methodology is well suited for exposing integration sensitivity and assessing coupled performance in physical architecture concepts --Abstract, page iii

Review of selection criteria for sensor and actuator configurations suitable for internal combustion engines

This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited. Simple control metrics such as conditioning number are popular, mostly because they need fewer assumptions than closed-loop metrics, which require a full plant, disturbance and goal model. Overall, no clear consensus can be found on the choice of metrics to define optimal control configurations, with physical measures, linear algebra metrics and modern control metrics all being used. Genetic algorithms and multi-criterial optimisation were identified as the most widely used methods for optimal sensor selection, although addressing the dimensionality and complexity of formulating the problem remains a challenge. This review does present a number of different successful approaches for specific applications domains, some of which may be applicable to diesel engines and other automotive applications. For a thorough treatment, non-linear dynamics and uncertainties need to be considered together, which requires sophisticated (non-Gaussian) stochastic models to establish the value of a control architecture

Extending the Exposure Score of Web Browsers by Incorporating CVSS

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Yet its content differs from one browser to another. Despite the privacy and security risks of User-Agent strings, very few works have tackled this problem. Our previous work proposed giving Internet browsers exposure relative scores to aid users to choose less intrusive ones. Thus, the objective of this work is to extend our previous work through: first, conducting a user study to identify its limitations. Second, extending the exposure score via incorporating data from the NVD. Third, providing a full implementation, instead of a limited prototype. The proposed system: assigns scores to users’ browsers upon visiting our website. It also suggests alternative safe browsers, and finally it allows updating the back-end database with a click of a button. We applied our method to a data set of more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available here [4].</p

Cognitive modeling of social behaviors

To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks —why people do what they do—to modeling behavioral patterns—what people do—as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitat’s layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training

Coupling Metaproteomics with Taxonomy to Determine Responses of Bacterioplankton to Organic Perturbations in the Western Arctic Ocean

Understanding how the functionality of marine microbial communities change over time and space, and which taxonomic groups dominate distinct metabolic pathways, are essential to understanding the ecology of these microbiomes and the factors contributing to their regulation of elemental cycles in the oceans. The primary goal of this dissertation was to investigate the community metabolic and taxonomic responses and the degradation potential of two compositionally distinct marine microbiomes within the shallow shelf ecosystem of the Chukchi Sea after rapid fluctuations in algal organic matter availability. Novel bioinformatics tools were collaboratively developed and used together with community proteomics (metaproteomics) to characterize and quantify changes in bacterial community functioning and taxonomic composition over time. 16S rRNA sequencing was employed to confirm bacterial taxonomic dynamics. These approaches were linked to particulate analyses for lipids and amino acids in order to track temporal changes in organic substrate composition. Results obtained using these improved methodological standards and the multidisciplinary approach demonstrated that organic perturbations within these systems stimulated changes to microbial taxonomic composition and functionality. The removal of organic particles seen within the control initiated a divergence between the two microbiomes while substrate abundance, as algal inputs, led to a convergence in community function. Despite the functional and taxonomic overlap seen as dominant features characterizing the responses to rapid influxes of algal organic matter, unique metabolic traits differentiated the major bacterial groups of each microbiome. This was most apparent in the recycling of nitrogen and carbon as well as substrate acquisition, suggesting that conditions which select for certain bacterial groups in the western Arctic Ocean may impact local chemical gradients. The large dataset of information obtained from this dissertation provides insight into the timing and characterization of Arctic bacterial community responses to environmental perturbations and in turn how they influence changes in substrate composition through selective degradation of labile lipid classes. In addition, this work demonstrates the applicability of trait-based methodologies to inform on how environmental conditions may drive niche formation within complex microbial communities

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Report of the 2004 Workshop on In Situ Iron Enrichment Experiments in the Eastern and Western Subarctic Pacific

Foreword 1. BACKGROUND AND OBJECTIVES (pdf, 0.1 Mb) 2. 2004 WORKSHOP SUMMARY (pdf, < 0.1 Mb) 2.1. What have we learned from the enrichment experiments? 2.2 What are the outstanding questions? 2.3 Recommendations for SEEDS-II 3. EXTENDED ABSTRACTS OF THE 2004 WORKSHOP 3.1 Synthesis of the Iron Enrichment Experiments: SEEDS and SERIES (pdf, 0.5 Mb) Iron fertilization experiment in the western subarctic Pacific (SEEDS) by Atsushi Tsuda The response of N and Si to iron enrichment in the Northeast Pacific Ocean: Results from SERIES by David Timothy, C.S. Wong, Yukihiro Nojiri, Frank A. Whitney, W. Keith Johnson and Janet Barwell-Clarke 3.2 Biological and Physiological Responses (pdf, 0.2 Mb) Zooplankton responses during SEEDS by Hiroaki Saito Phytoplankton community response to iron and temperature gradient in the NW and NE subarctic Pacific Ocean by Isao Kudo, Yoshifumi Noiri, Jun Nishioka, Hiroshi Kiyosawa and Atsushi Tsuda SERIES: Copepod grazing on diatoms by Frank A. Whitney, Moira Galbraith, Janet Barwell-Clarke and Akash Sastri The Southern Ocean Iron Enrichment Experiment: The nitrogen uptake response by William P. Cochlan and Raphael M. Kudela 3.3 Biogeochemical Responses (pdf, 0.5 Mb) What have we learned regarding iron biogeochemistry from iron enrichment experiments? by Jun Nishioka, Shigenobu Takeda and W. Keith Johnson Iron dynamics and temporal changes of iron speciation in SERIES by W. Keith Johnson, C.S. Wong, Nes Sutherland and Jun Nishioka Dissolved organic matter dynamics during SEEDS and SERIES experiments by Takeshi Yoshimura and Hiroshi Ogawa Formation of transparent exopolymer particles during the in-situ iron enrichment experiment in the western subarctic Pacific (SEEDS) by Shigenobu Takeda, Neelam Ramaiah, Ken Furuya and Takeshi Yoshimura Atmospheric measurement by Mitsuo Uematsu 3.4 Prediction from Models (pdf, 0.3 Mb) Modelling iron limitation in the North Pacific by Kenneth L. Denman and M. Angelica Peña A proposed model of the SERIES iron fertilization patch by Debby Ianson, Christoph Voelker and Kenneth L. Denman 4. LIST OF PARTICIPANTS FOR THE 2004 WORKSHOP (pdf, < 0.1 Mb) APPENDIX 1 Report of the 2000 Planning Workshop on Designing the Iron Fertilization Experiment in the Subarctic Pacific (pdf, 1 Mb) APPENDIX 2 Terms of Reference for the Advisory Panel on Iron fertilization experiment in the subarctic Pacific Ocean (pdf, < 0.1 Mb) APPENDIX 3 Historical List of Advisory Panel Members on Iron fertilization experiment in the subarctic Pacific Ocean (pdf, < 0.1 Mb) APPENDIX 4 IFEP-AP Annual Reports (pdf, 0.1 Mb) APPENDIX 5 PICES Press Articles (pdf, 0.6 Mb) (194 page document

Computational roles of cortico-cerebellar loops in temporal credit assignment

Animal survival depends on behavioural adaptation to the environment. This is thought to be enabled by plasticity in the neural circuit. However, the laws which govern neural plasticity are unclear. From a functional aspect, it is desirable to correctly identify, or assign “credit” for, the neurons or synapses responsible for the task decision and subsequent performance. In the biological circuit, the intricate, non-linear interactions involved in neural networks makes appropriately assigning credit to neurons highly challenging. In the temporal domain, this is known as the temporal credit assignment (TCA) problem. This Thesis considers the role the cerebellum – a powerful subcortical structure with strong error-guided plasticity rules – as a solution to TCA in the brain. In particular, I use artificial neural networks as a means to model and understand the mechanisms by which the cerebellum can support learning in the neocortex via the cortico-cerebellar loop. I introduce two distinct but compatible computational models of cortico-cerebellar interaction. The first model asserts that the cerebellum provides the neocortex predictive feedback, modeled in the form of error gradients, with respect to its current activity. This predictive feedback enables better credit assignment in the neocortex and effectively removes the lock between feedforward and feedback processing in cortical networks. This model captures observed long-term deficits associated with cerebellar dysfunction, namely cerebellar dysmetria, in both the motor and non-motor domain. Predictions are also made with respect to alignment of cortico-cerebellar activity during learning and the optimal task conditions for cerebellar contribution. The second model also looks at the role of the cerebellum in learning, but now considers its ability to instantaneously drive the cortex towards desired task dynamics. Unlike the first model, this model does not assume any local cortical plasticity need take place at all and task-directed learning can effectively be outsourced to the cerebellum. This model captures recent optogenetic studies in mice which show the cerebellum as a necessary component for the maintenance of desired cortical dynamics and ensuing behaviour. I also show that this driving input can eventually be used as a teaching signal for the cortical circuit, thereby conceptually unifying the two models. Overall, this Thesis explores the computational role of the cerebellum and cortico-cerebellar loops for task acquisition and maintenance in the brain