Relationship between work stress and body mass index among 45,810 female and male employees

Objective: The proportion of overweight and obese people has grown rapidly, and obesity has now been widely recognized as an important public health problem. At the came time, stress has increased in working life. The 2 problems could be connected if work stress promotes unhealthy eating habits and sedentary behavior and thereby contributes to weight gain. This study explored the association between work stress and body mass index (BMI; kg/m(2)). Methods: We used cross-sectional questionnaire data obtained from 45,810 female and male employees participating in the ongoing Finnish Public Sector Cohort Study. We constructed individual-level scores, as well as occupational- and organizational-level aggregated scores for work stress, as indicated by the demand/control model and the effort-reward imbalance model. Linear regression analyses were stratified by sex and socioeconomic status (SES) and adjusted for age, marital status, job contract, smoking, alcohol consumption, physical activity, and negative affectivity. Results: The results with the aggregated scores showed that lower job control, higher job strain, and higher effort-reward imbalance were associated with a higher BMI. In men, lower job demands were also associated with a higher BMI. These associations were not accounted for by SES, although an additional adjustment for SES attenuated the associations. The results obtained with the individual-level scores were in the same direction, but the relationships were weaker than those obtained with the aggregated scores. Conclusions: This study shows a weak association between work stress and BMI.

Work stress, smoking status, and smoking intensity: an observational study of 46 190 employees

Study objective: To examine the relation between work stress, as indicated by the job strain model, and the effort-reward imbalance model, and smoking.

The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

This paper introduces the discrete choice model-paradigm of Random Regret Minimisation (RRM) to the field of health economics. The RRM is a regret-based model that explores a driver of choice different from the traditional utility-based Random Utility Maximisation (RUM). The RRM approach is based on the idea that, when choosing, individuals aim to minimise their regret–regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. Analysing data from a discrete choice experiment on diet, physical activity and risk of a fatal heart attack in the next ten years administered to a sample of the Northern Ireland population, we find that the combined use of RUM and RRM models offer additional information, providing useful behavioural insights for better informed policy appraisal.

Application of a mechanobiological simulation technique to stents used clinically

Many cardiovascular diseases are characterised by the restriction of blood flow through arteries. Stents can be expanded within arteries to remove such restrictions; however, tissue in-growth into the stent can lead to restenosis. In order to predict the long-term efficacy of stenting, a mechanobiological model of the arterial tissue reaction to stress is required. In this study, a computational model of arterial tissue response to stenting is applied to three clinically relevant stent designs. We ask the question whether such a mechanobiological model can differentiate between stents used clinically, and we compare these predictions to a purely mechanical analysis. In doing so, we are testing the hypothesis that a mechanobiological model of arterial tissue response to injury could predict the long-term outcomes of stent design. Finite element analysis of the expansion of three different stent types was performed in an idealised, 3D artery. Injury was calculated in the arterial tissue using a remaining-life damage mechanics approach. The inflammatory response to this initial injury was modelled using equations governing variables which represented tissue-degrading species and growth factors. Three levels of inflammation response were modelled to account for inter-patient variability. A lattice-based model of smooth muscle cell behaviour was implemented, treating cells as discrete agents governed by local rules. The simulations predicted differences between stent designs similar to those found in vivo. It showed that the volume of neointima produced could be quantified, providing a quantitative comparison of stents. In contrast, the differences between stents based on stress alone were highly dependent on the choice of comparison criteria. These results show that the choice of stress criteria for stent comparisons is critical. This study shows that mechanobiological modelling may provide a valuable tool in stent design, allowing predictions of their long-term efficacy. The level of inflammation was shown to affect the sensitivity of the model to stent design. If this finding was verified in patients, this could suggest that high-inflammation patients may require alternative treatments to stenting.

Emerging asymmetric interactions between forage and predator fisheries impose management trade-offs

A size and trait-based marine community model was used to investigate interactions, with potential implications for yields, when a fishery targeting forage fish species (whose main adult diet is zooplankton) co-occurs with a fishery targeting larger-sized predator species. Predicted effects on the size structure of the fish community, growth and recruitment of fishes, and yield from the fisheries were used to identify management trade-offs among the different fisheries. Results showed that moderate fishing on forage fishes imposed only small effects on predator fisheries, whereas predator fisheries could enhance yield from forage fisheries under some circumstances.

Discriminating Taxonomic Categories and Domains in Mental Simulations of Concepts of Varying Concreteness

Most studies of conceptual knowledge in the brain focus on a narrow range of concrete conceptual categories, rely on the researchers' intuitions about which object belongs to these categories, and assume a broadly taxonomic organization of knowledge. In this fMRI study, we focus on concepts with a variety of concreteness levels; we use a state of the art lexical resource (WordNet 3.1) as the source for a relatively large number of category distinctions and compare a taxonomic style of organization with a domain-based model (associating concepts with scenarios). Participants mentally simulated situations associated with concepts when cued by text stimuli. Using multivariate pattern analysis, we find evidence that all Taxonomic categories and Domains can be distinguished from fMRI data and also observe a clear concreteness effect: Tools and Locations can be reliably predicted for unseen participants, but less concrete categories (e.g., Attributes, Communications, Events, Social Roles) can only be reliably discriminated within participants. A second concreteness effect relates to the interaction of Domain and Taxonomic category membership: Domain (e.g., relation to Law vs. Music) can be better predicted for less concrete categories. We repeated the analysis within anatomical regions, observing discrimination between all/most categories in the left middle occipital and temporal gyri, and more specialized discrimination for concrete categories Tool and Location in the left precentral and fusiform gyri, respectively. Highly concrete/abstract Taxonomic categories and Domain were segregated in frontal regions. We conclude that both Taxonomic and Domain class distinctions are relevant for interpreting neural structuring of concrete and abstract concepts.

Emotional insecurity in the family and community and youth delinquency in Northern Ireland: A person-oriented analysis across five-waves

Background: Over one billion children are exposed worldwide to political violence and armed conflict. Currently, conclusions about bases for adjustment problems are qualified by limited longitudinal research from a process-oriented, social-ecological perspective. In this study, we examined a theoretically-based model for the impact of multiple levels of the social ecology (family, community) on adolescent delinquency. Specifically, this study explored the impact of children’s emotional insecurity about both the family and community on youth delinquency in Northern Ireland. Methods: In the context of a five-wave longitudinal research design, participants included 999 mother-child dyads in Belfast (482 boys, 517 girls), drawn from socially-deprived, ethnically-homogenous areas that had experienced political violence. Youth ranged in age from 10 to 20 and were 12.18 (SD = 1.82) years old on average at Time 1. Findings: The longitudinal analyses were conducted in hierarchical linear modeling (HLM), allowing for the modeling of inter-individual differences in intra-individual change. Intra-individual trajectories of emotional insecurity about the family related to children’s delinquency. Greater insecurity about the community worsened the impact of family conflict on youth’s insecurity about the family, consistent with the notion that youth’s insecurity about the community sensitizes them to exposure to family conflict in the home. Conclusions: The results suggest that ameliorating children’s insecurity about family and community in contexts of political violence is an important goal toward improving adolescents’ well-being, including reduced risk for delinquency.

Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia

In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra's algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.

An aggregated fridge-freezer peak shaving and valley filling control strategy for enhanced grid operations

The need for fast response demand side participation (DSP) has never been greater due to increased wind power penetration. White domestic goods suppliers are currently developing a `smart' chip for a range of domestic appliances (e.g. refrigeration units, tumble dryers and storage heaters) to support the home as a DSP unit in future power systems. This paper presents an aggregated population-based model of a single compressor fridge-freezer. Two scenarios (i.e. energy efficiency class and size) for valley filling and peak shaving are examined to quantify and value DSP savings in 2020. The analysis shows potential peak reductions of 40 MW to 55 MW are achievable in the Single wholesale Electricity Market of Ireland (i.e. the test system), and valley demand increases of up to 30 MW. The study also shows the importance of the control strategy start time and the staggering of the devices to obtain the desired filling or shaving effect.

Context-dependent Combination of Sensor Information in Dempster-Shafer Theory for BDI

There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.

Adapting to time: duration channels do not mediate human time perception

Accurately encoding the duration and temporal order of events is essential for survival and important to everyday activities, from holding conversations to driving in fast flowing traffic. Although there is a growing body of evidence that the timing of brief events (< 1s) is encoded by modality-specific mechanisms, it is not clear how such mechanisms register event duration. One approach gaining traction is a channel-based model; this envisages narrowly-tuned, overlapping timing mechanisms that respond preferentially to different durations. The channel-based model predicts that adapting to a given event duration will result in overestimating and underestimating the duration of longer and shorter events, respectively. We tested the model by having observers judge the duration of a brief (600ms) visual test stimulus following adaptation to longer (860ms) and shorter (340ms) stimulus durations. The channel-based model predicts perceived duration compression of the test stimulus in the former condition and perceived duration expansion in the latter condition. Duration compression occurred in both conditions, suggesting that the channel-based model does not adequately account for perceived duration of visual events.

TELL ME Design: Protective Behaviour During an Epidemic

The TELL ME agent based model simulates the connections between health agency communication, personal decisions to adopt protective behaviour during an influenza epidemic, and the effect of those decisions on epidemic progress. The behaviour decisions are modelled with a combination of personal attitude, behaviour adoption by neighbours, and the local recent incidence of influenza. This paper sets out and justifies the model design, including how these decision factors have been operationalised. By exploring the effects of different communication strategies, the model is intended to assist health authorities with their influenza epidemic communication plans. It can both assist users to understand the complex interactions between communication, personal behaviour and epidemic progress, and guide future data collection to improve communication planning.

Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

In this paper we present a convolutional neuralnetwork (CNN)-based model for human head pose estimation inlow-resolution multi-modal RGB-D data. We pose the problemas one of classification of human gazing direction. We furtherfine-tune a regressor based on the learned deep classifier. Next wecombine the two models (classification and regression) to estimateapproximate regression confidence. We present state-of-the-artresults in datasets that span the range of high-resolution humanrobot interaction (close up faces plus depth information) data tochallenging low resolution outdoor surveillance data. We buildupon our robust head-pose estimation and further introduce anew visual attention model to recover interaction with theenvironment. Using this probabilistic model, we show thatmany higher level scene understanding like human-human/sceneinteraction detection can be achieved. Our solution runs inreal-time on commercial hardware

Control Strategues for Automatic Generation Control over MTDC Grid

Increasingly in power systems, there is a trend towards the sharing of reserves and integration of markets over wide areas in order to enable increased penetration of renewable sources in interconnected power systems. In this paper, a number of simple PI and gain based Model Predictive Control algorithms are proposed for Automatic Generation Control in AC areas connected to Multi-Terminal Direct Current grids. The paper discusses how this approach improves the sharing of secondary reserves and could assist in achieving EU energy targets for 2030 and beyond.