Security Awareness in the Internet of Everything

Our societal infrastructure is transforming into a connected cyber-physical system of systems, providing numerous opportunities and new capabilities, yet also posing new and reinforced risks that require explicit consideration. This chapter addresses risks specifically related to cyber-security. One contributing factor, often neglected, is the level of security education of the users. Another factor, often overlooked, concerns security-awareness of the engineers developing cyber-physical systems. Authors present results of interviews with developers and surveys showing that increase in security-awareness and understanding of security risks, evaluated as low, are the first steps to mitigate the risks. Authors also conducted practical evaluation investigating system connectivity and vulnerabilities in complex multi-step attack scenarios. This chapter advocates that security awareness of users and developers is the foundation to deployment of interconnected system of systems, and provides recommendations for steps forward highlighting the roles of people, organizations and authorities.

A functional view reveals substantial predictability of pollinator-mediated selection

A predictive understanding of adaptation to changing environments hinges on a mechanistic understanding of the extent and causes of variation in natural selection. Estimating variation in selection is difficult due to the complex relationships between phenotypic traits and fitness, and the uncertainty associated with individual selection estimates. Plant-pollinator interactions provide ideal systems for understanding variation in selection and its predictability, because both the selective agents (pollinators) and the process linking phenotypes to fitness (pollination) are generally known. Through examples from the pollination literature, I discuss how explicit consideration of the functional mechanisms underlying trait-performance relationships can clarify the relationship between traits and fitness, and how variation in the ecological context that generates selection can help disentangle biologically important variation in selection from sampling variation. I then evaluate the predictability of variation in pollinator-mediated selection through a survey, reanalysis, and synthesis of results from the literature. The synthesis demonstrates that pollinator-mediated selection often varies substantially among trait functional groups, as well as in time and space. Covariance between patterns of selection and ecological variables provides additional support for the biological importance of observed selection, but the detection of such covariance depends on careful choice of relevant predictor variables as well as consideration of quantitative measurements and their meaning, an aspect often neglected in selection studies.

Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.

Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper

Dense blooms of filamentous diazotrophic cyanobacteria are formed every summer in the Baltic Sea. These autotrophic organisms may bypass nitrogen limitation by performing nitrogen fixation, which also governs surrounding organisms by increasing bioavailable nitrogen. The magnitude of the nitrogen fixation is important to estimate from a management perspective since this might counteract eutrophication reduction measures. Here, a cyanobacteria life cycle model has been implemented for the first time in a high-resolution 3D coupled physical and biogeochemical model of the Baltic Sea, spanning the years 1850–2008. The explicit consideration of life cycle dynamics and transitions significantly improves the representation of the cyanobacterial phenological patterns compared to earlier 3D modeling efforts. Now, the rapid increase and decrease in cyanobacteria in the Baltic Sea are well captured, and the seasonal timing is in concert with observations. The current improvement also had a large effect on the nitrogen fixation load and is now in agreement with estimates based on in situ measurements. By performing four phosphorus sensitivity runs, we demonstrate the importance of both organic and inorganic phosphorus availability for historical cyanobacterial biomass estimates. The model combination can be used to continuously predict internal nitrogen loads via nitrogen fixation in Baltic Sea ecosystem management, which is of extra importance in a future ocean with changed conditions for the filamentous cyanobacteria.

Conceptual and Theoretical Issues in Forensic Neuropsychology

<p>Neuroscience is an increasingly popular area of study in forensic psychology, and there is a large body of empirical research emerging investigating the biological basis of offending behaviour. However, the theoretical and conceptual underpinnings of forensic neuroscience are currently underdeveloped. The aim of this thesis is to provide insight into the potential issues with forensic neuroscience and provide a number of suggestions for researchers to follow. This thesis begins by outlining these theoretical, conceptual, and empirical issues that researchers should be considering, including conceptualisation of the mind, explanation, and the methodological issues in neuroscience. These issues are then examined in more detail using two specific subject areas as exemplars: deception detection and mind-reading by brain-reading. This thesis concludes with suggestions for future researchers, which include making sure that research is based on a strong theoretical framework, clarity around the kind of explanation employed and use of explanatory pluralism, clear and consistent definitions to improve conceptual validity, using consistent and conceptually valid experimental protocols, and explicit consideration of technical limitations and how they impact the validity of the experiment.</p>

Conceptual and Theoretical Issues in Forensic Neuropsychology

<p>Neuroscience is an increasingly popular area of study in forensic psychology, and there is a large body of empirical research emerging investigating the biological basis of offending behaviour. However, the theoretical and conceptual underpinnings of forensic neuroscience are currently underdeveloped. The aim of this thesis is to provide insight into the potential issues with forensic neuroscience and provide a number of suggestions for researchers to follow. This thesis begins by outlining these theoretical, conceptual, and empirical issues that researchers should be considering, including conceptualisation of the mind, explanation, and the methodological issues in neuroscience. These issues are then examined in more detail using two specific subject areas as exemplars: deception detection and mind-reading by brain-reading. This thesis concludes with suggestions for future researchers, which include making sure that research is based on a strong theoretical framework, clarity around the kind of explanation employed and use of explanatory pluralism, clear and consistent definitions to improve conceptual validity, using consistent and conceptually valid experimental protocols, and explicit consideration of technical limitations and how they impact the validity of the experiment.</p>

The scientific study of consciousness cannot, and should not, be morally neutral

Normative decisions about moral status are strongly coupled with beliefs and assumptions about consciousness. Whether an individual is able to experience their environment, feel pain and pleasure or reflect on their own experiences, have all been judged at some point as relevant to the moral question of whether they should be protected by law. The scientific study of consciousness has advanced our understanding of some of these empirical questions by revealing the capacities and limits of these dimensions of consciousness in different states and animal species. In light of the tight link with moral status, scientific discoveries in this field have direct implications on law and ethics. Furthermore, this link with ethics may place implicit pressure on the scientific community studying consciousness to justify current societal norms, rather than challenge them. Finally, given the important role of consciousness in determining moral standing, the use of non-human animals in the scientific study of consciousness introduces a direct conflict between scientific relevance and ethics – the more scientifically valuable an animal model is for studying consciousness, the more difficult it becomes to ethically justify compromises to its well-being for consciousness research. Here we call for a discussion of the immediate ethical corollaries of the body of knowledge that has been accumulated, and for a more explicit consideration of the role of ideology and ethics in the scientific study of consciousness, including the question of animal models of consciousness.

Using structural break inference for forecasting time series

Rather than relying on a potentially poor point estimate of a coefficient break date when forecasting, this paper proposes averaging forecasts over sub-samples indicated by a confidence interval or set for the break date. Further, we examine whether explicit consideration of a possible variance break and the use of a two-step methodology improves forecast accuracy compared with using heteroskedasticity robust inference. Our Monte Carlo results and empirical application to US productivity growth show that averaging using the likelihood ratio-based confidence set typically performs well in comparison with other methods, while two-step inference is particularly useful when a variance break occurs concurrently with or after any coefficient break.

Price Discovery of Consignment Auctions for Emission Permits

A consignment auction aims to increase political feasibility by reducing the financial burden of initial permits allocation and to do the role of price discovery. However, previous analytical models presented contradictory results for the price discovery function of a consignment auction. Thus, this study reexamines whether a consignment auction can perform its price discovery function. The study uses a simple game model with several assumptions differentiated from previous analytical models: explicit consideration of the secondary market and firms as price-takers with various behaviors to respond to uncertainty about the price in the secondary market. Firms are classified into three types: speculators who seek arbitrage, doctrinarians who determine a permit demand based on an estimation of their marginal abatement cost, and neutralists who keep a permit demand the same as initial emission endowments. The results reveal that when a consignment auction was introduced, the expected equilibrium price was identical to that of the secondary market price, demonstrating that the auction could deliver the price discovery function. This is because speculators and doctrinarians provide information about their price expectations and marginal abatement cost through their estimated demand functions. Additionally, the smaller number of neutralists is, and the higher the risk-seeking propensity of speculators is, the more effective the price discovery function is.

Grand challenges in biodiversity–ecosystem functioning research in the era of science–policy platforms require explicit consideration of feedbacks

Feedbacks are an essential feature of resilient socio-economic systems, yet the feedbacks between biodiversity, ecosystem services and human wellbeing are not fully accounted for in global policy efforts that consider future scenarios for human activities and their consequences for nature. Failure to integrate feedbacks in our knowledge frameworks exacerbates uncertainty in future projections and potentially prevents us from realizing the full benefits of actions we can take to enhance sustainability. We identify six scientific research challenges that, if addressed, could allow future policy, conservation and monitoring efforts to quantitatively account for ecosystem and societal consequences of biodiversity change. Placing feedbacks prominently in our frameworks would lead to (i) coordinated observation of biodiversity change, ecosystem functions and human actions, (ii) joint experiment and observation programmes, (iii) more effective use of emerging technologies in biodiversity science and policy, and (iv) a more inclusive and integrated global community of biodiversity observers. To meet these challenges, we outline a five-point action plan for collaboration and connection among scientists and policymakers that emphasizes diversity, inclusion and open access. Efforts to protect biodiversity require the best possible scientific understanding of human activities, biodiversity trends, ecosystem functions and—critically—the feedbacks among them.