Effects of variations in water quantity and quality in the structure and functions of invertebrates’ community of a Mediterranean urban stream

Urban Ecosystems - Urban streams provide important ecosystem services to cities’ population, from the maintenance of urban biodiversity, temperature, humidity and air quality to improving...     

Influence of door handles design in effort perception: accessibility and usability

The application of ergonomics in product design is essential to its accessibility and usability. The development of manual devices should be based on ergonomic principles. Effort perception analysis is an essential approach to understand the physical and subjective aspects of the interface. The objective of the present study was to analyze the effort perception during a simulated task with different door handles by Portuguese subjects of both genders and different ages. This transversal study agreed with ethical aspects. 180 subjects of both genders pertaining to three age groups have participated. Five door handles with different shapes were evaluated. A subjective numeric rating scale of 5 levels was used to evaluate the effort. For statistical analysis it was applied the Friedman non-parametric test. The results have showed no significant differences of effort perception in door handles "A" and "B"; "A" and "D"; and "D" and "C". Door handle "E" presented the lowest values of all. In general, there's an inverse relationship between the results of biomechanical studies and the effort perception of the same task activity. This shows that door handles design influence directly these two variables and can interfere in the accessibility and usability of these kinds of products.     

Characterizing Environmental Harm: Developments in an Approach to Strategic Risk Assessment and Risk Management

Environmental policymakers and regulators are often in the position of having to prioritize their actions across a diverse range of environmental pressures to secure environmental protection and improvements. Information on environmental issues to inform this type of strategic analysis can be disparate; it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy analysis and comparison. Nevertheless, judgments are required on the significance of various environmental pressures and on the inherent uncertainties to inform strategic assessments such as “state of the environment” reports. How can decisionmakers go about this type of strategic and comparative risk analysis? In an attempt to provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has conducted a program of developmental research on strategic risk assessment since 1996. The tools developed under this program use the concept of “environmental harm” as a common metric, viewed from technical, social, and economic perspectives, to analyze impacts from a range of environmental pressures. Critical to an informed debate on the relative importance of these perspectives is an understanding and analysis of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to actual or potential environmental harm from a range of hazards. Recent developments in our approach, described herein, allow a presentation of the analysis in a structured fashion so as to better inform risk‐management decisions.     

Sequential parameter learning and filtering in structured autoregressive state-space models

We present particle-based algorithms for sequential filtering and parameter learning in state-space autoregressive (AR) models with structured priors. Non-conjugate priors are specified on the AR coefficients at the system level by imposing uniform or truncated normal priors on the moduli and wavelengths of the reciprocal roots of the AR characteristic polynomial. Sequential Monte Carlo algorithms are considered and implemented for on-line filtering and parameter learning within this modeling framework. More specifically, three SMC approaches are considered and compared by applying them to data simulated from different state-space AR models. An analysis of a human electroencephalogram signal is also presented to illustrate the use of the structured state-space AR models in describing biomedical signals.     

Bayesian factor models in characterizing molecular adaptation

Assessing the selective influence of amino acid properties is important in understanding evolution at the molecular level. A collection of methods and models has been developed in recent years to determine if amino acid sites in a given DNA sequence alignment display substitutions that are altering or conserving a prespecified set of amino acid properties. Residues showing an elevated number of substitutions that favorably alter a physicochemical property are considered targets of positive natural selection. Such approaches usually perform independent analyses for each amino acid property under consideration, without taking into account the fact that some of the properties may be highly correlated. We propose a Bayesian hierarchical regression model with latent factor structure that allows us to determine which sites display substitutions that conserve or radically change a set of amino acid properties, while accounting for the correlation structure that may be present across such properties. We illustrate our approach by analyzing simulated data sets and an alignment of lysin sperm DNA.     

Measuring and Analysing Community Action for Health: An Indicator-Based Typology and Its Application to the Case of Barcelona

Social welfare systems face major challenges, particularly in a context of social transformation, austerity and growing inequalities. This process is highly visible in the health sector. In this context, many voices ask for public sector reforms and community action for health as a relevant practice. However, analyses and evaluations of this kind of practices are still limited, particularly beyond the cases of single community health actions or interventions. We still need to identify key indicators for measuring and characterising what community action for health consists of, as well as to what degree this kind of intervention has been developed across a city. Based on a research about 49 neighborhoods in Barcelona, this paper creates an index to measure and characterize community action for health, using different indicators: citizen engagement programs in community health, organizational transformation of the health and social protection systems, stable participatory structures with specific teams, and urban health policies. We apply the index to the case of Barcelona and build a map of community action for health in the city using 4 categories: strong community health development (one neighbourhood), middle (9 neighborhoods), emergent (25 neighborhoods) and without specific community health promotion (14 neighbourhoods). We find that community action for health is extensive within the city of Barcelona, have great potential as a response to the need for change in the relationship between the public (health) sector and the citizenry, but is still implemented unequally across the urban territory in terms of types and methods.     

Portuguese adopted adolescents' perception of attachment relationships to parents

The establishment of emotional bonds is one of the most important tasks of the adoptive family. Most research about attachment in adoption focuses on young adopted children, as opposed to adoptees in other stages of development. The present study aims at assessing the adopted adolescents' self‐perception of attachment relationships with their adoptive parents, by pairing them with a group of institutionalized adolescents and another one of adolescents in the community. One hundred sixty‐five adolescents (55 adopted, 55 in residential care, and 55 living with their birth family), aged 12 to 19, participated in this study. Data were collected using the Inventory of Parent and Peer Attachment to assess attachment relationships with parents on three dimensions: trust, communication, and alienation. The results showed that adoptees perceived their relationship with their parents in a similar way to peers in the community and presented higher results when compared to institutionalized adolescents in trust and communication and lower scores in alienation. The results highlighted the relevance of family context experiences and suggested that adoption can offer the possibility of building a secure attachment relationship, which is not the case in the context of collective care, as happens within institutionalized care.     

Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag–lead structure

Multiple time series of scalp electrical potential activity are generated routinely in electroencephalographic (EEG) studies. Such recordings provide important non-invasive data about brain function in human neuropsychiatric disorders. Analyses of EEG traces aim to isolate characteristics of their spatiotemporal dynamics that may be useful in diagnosis, or may improve the understanding of the underlying neurophysiology or may improve treatment through identifying predictors and indicators of clinical outcomes. We discuss the development and application of non-stationary time series models for multiple EEG series generated from individual subjects in a clinical neuropsychiatric setting. The subjects are depressed patients experiencing generalized tonic–clonic seizures elicited by electroconvulsive therapy (ECT) as antidepressant treatment. Two varieties of models—dynamic latent factor models and dynamic regression models—are introduced and studied. We discuss model motivation and form, and aspects of statistical analysis including parameter identifiability, posterior inference and implementation of these models via Markov chain Monte Carlo techniques. In an application to the analysis of a typical set of 19 EEG series recorded during an ECT seizure at different locations over a patient's scalp, these models reveal time-varying features across the series that are strongly related to the placement of the electrodes. We illustrate various model outputs, the exploration of such time-varying spatial structure and its relevance in the ECT study, and in basic EEG research in general.     

Evaluating the Move to a Linear Tax System in Germany and Other European Countries

This paper proposes a comparison of the results of tax policy analysis obtained on the basis of unitary and collective representations of the household. We first generate labour supplies consistent with the collective rationality, by use of a model calibrated on microdata as described in Vermeulen et al. [Collective Models of Household Labor Supply with Nonconvex Budget Sets and Nonparticipation: A Calibration Approach (2006)]. A unitary model is then estimated on these collective data and unitary and collective responses to a tax reform are compared. We focus on the introduction of linear taxation in Germany. The exercise is replicated for other European countries and other topical reforms. Distortions due to the use of a unitary model turn out to be important in predicting labour supply adjustments, in the design of tax revenue neutral reforms, and in predicting a reform’s welfare implications.