Rate of Vegetation Recovery in Restored Prairie Wetlands

Wetlands are restored to compensate for wetland loss and degradation. To determine the potential rate and success of vegetation recovery in restored wetlands, prairie wetlands of different restoration ages (3 to 23 years since restoration), including drained and natural (embedded within both agricultural and protected landscape), were sampled for vegetation in Alberta, Canada. Vegetation was assessed based on species richness, percentage and cover of hydrophytes, natives and non-natives, and community composition. Analysis of covariance with wetland area as a covariate and non-metric multidimensional scaling results indicated that restored wetlands resembled low-integrity natural wetlands that occurred on agricultural landscapes within 3-5 years of restoration. However, restored wetlands differed in community composition when compared to high-integrity natural wetlands that occurred on protected landscapes. Early establishment of non-native species during recovery, dispersal limitation, and depauperated native seedbank were probable barriers to successful recovery. This differential success of vegetation recovery highlights the need for improved region-specific wetland restoration actions

Error-efficient computing systems

This survey explores the theory and practice of techniques to make computing systems faster or more energy-efficient by allowing them to make controlled errors. In the same way that systems which only use as much energy as necessary are referred to as being energy-efficient, you can think of the class of systems addressed by this survey as being error-efficient: They only prevent as many errors as they need to. The definition of what constitutes an error varies across the parts of a system. And the errors which are acceptable depend on the application at hand. In computing systems, making errors, when behaving correctly would be too expensive, can conserve resources. The resources conserved may be time: By making some errors, systems may be faster. The resource may also be energy: A system may use less power from its batteries or from the electrical grid by only avoiding certain errors while tolerating benign errors that are associated with reduced power consumption. The resource in question may be an even more abstract quantity such as consistency of ordering of the outputs of a system. This survey is for anyone interested in an end-to-end view of one set of techniques that address the theory and practice of making computing systems more efficient by trading errors for improved efficiency

Multi-attribute tradespace exploration for survivability

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 235-249).Survivability is the ability of a system to minimize the impact of a finite-duration disturbance on value delivery (i.e., stakeholder benefit at cost), achieved through (1) the reduction of the likelihood or magnitude of a disturbance, (2) the satisfaction of a minimally acceptable level of value delivery during and after a disturbance, and/or (3) a timely recovery. Traditionally specified as a requirement in military systems, survivability is an increasingly important consideration for all engineering systems given the proliferation of natural and artificial threats. Although survivability is an emergent system property that arises from interactions between a system and its environment, conventional approaches to survivability engineering are reductionist in nature. Furthermore, current methods neither accommodate dynamic threat environments nor facilitate stakeholder communication for conducting trade-offs among system lifecycle cost, mission utility, and operational survivability. Multi-Attribute Tradespace Exploration (MATE) for Survivability is introduced as a system analysis methodology to improve the generation and evaluation of survivable alternatives during conceptual design. MATE for Survivability applies decision theory to the parametric modeling of thousands of design alternatives across representative distributions of disturbance environments. To improve the generation of survivable alternatives, seventeen empirically-validated survivability design principles are introduced. The general set of design principles allows the consideration of structural and behavioral strategies for mitigating the impact of disturbances over the lifecycle of a given encounter.(cont.) To improve the evaluation of survivability, value-based metrics are introduced for the assessment of survivability as a dynamic, continuous, and path-dependent system property. Two of these metrics, time-weighted average utility loss and threshold availability, are used to evaluate survivability based on the relationship between stochastic utility trajectories of system state and stakeholder expectations across nominal and perturbed environments. Finally, the survivability "tear(drop)" tradespace is introduced to enable the identification of inherently survivable architectures that efficiently balance performance metrics of cost, utility, and survivability. The internal validity and prescriptive value of the design principles, metrics, and tradespaces comprising MATE for Survivability are established through applications to the designs of an orbital transfer vehicle and a satellite radar system.by Matthew G. Richards.Ph.D

Mastering Uncertainty in Mechanical Engineering

This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master uncertainty in engineering design, production and product usage alike. It gathers authoritative contributions by more than 30 scientists reporting on years of research in the areas of engineering, applied mathematics and law, thus offering a timely, comprehensive and multidisciplinary account of theories and methods for quantifying data, model and structural uncertainty, and of fundamental strategies for mastering uncertainty. It covers key concepts such as robustness, flexibility and resilience in detail. All the described methods, technologies and strategies have been validated with the help of three technical systems, i.e. the Modular Active Spring-Damper System, the Active Air Spring and the 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research. Overall, this book offers a timely, practice-oriented reference guide to graduate students, researchers and professionals dealing with uncertainty in the broad field of mechanical engineering

Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Process Resilience Analysis Framework for Design and Operations

Process plants are complex socio-technical systems that degrade gradually and change with advancing technology. This research deals with exploring and answering questions related to the uncertainties involved in the process systems, and their complexity. It aims to systematically integrate resilience in process design and operations through three different phases of prediction, survival, and recovery using a novel framework called Process Resilience Analysis Framework (PRAF). The analysis relies on simulation, data-driven models and optimization approach employing the resilience metrics developed in this research. In particular, an integrated method incorporating aspects of process operations, equipment maintenance, and process safety is developed for the following three phases: •Prediction: to find the feasible operating region under changing conditions using Bayesian approach, global sensitivity analysis, and robust simulation methods, •Survival: to determine optimal operations and maintenance strategies using simulation, Bayesian regression analysis, and optimization, and •Recovery: to develop a strategy for emergency barriers in abnormal situations using dynamic simulation, Bayesian analysis, and optimization. Examples of a batch reactor, and cooling tower operations process unit are used to illustrate the application of PRAF. The results demonstrate that PRAF is successful in capturing the interactions between the process operability characteristics, maintenance, and safety policy. The prediction phase analysis leads to good dynamic response and stability of operations. The survival phase helps in the reduction of unplanned shutdown and downtime. The recovery phase results in in reduced severity of consequences, and response time and overall enhanced recovery. Overall, PRAF achieves flexibility, controllability and reliability of the system, supports more informed decision-making and profitable process systems

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen