A framework for coupling explanation and prediction in hydroecological modelling

Causal explanation and empirical prediction are usually addressed separately when modelling ecological systems. This potentially leads to erroneous conflation of model explanatory and predictive power, to predictive models that lack ecological interpretability, or to limited feedback between predictive modelling and theory development. These are fundamental challenges to appropriate statistical and scientific use of ecological models. To help address such challenges, we propose a novel, integrated modelling framework which couples explanatory modelling for causal understanding and input variable selection with a machine learning approach for empirical prediction. Exemplar datasets from the field of freshwater ecology are used to develop and evaluate the framework, based on 267 stream and river monitoring stations across England, UK. These data describe spatial patterns in benthic macroinvertebrate community indices that are hypothesised to be driven by meso-scale physical and chemical habitat conditions. Whilst explanatory models developed using structural equation modelling performed strongly (r² for two macroinvertebrate indices = 0.64–0.70), predictive models based on extremely randomised trees demonstrated moderate performance (r² for the same indices = 0.50–0.61). However, through coupling explanatory and predictive components, our proposed framework yields ecologically-interpretable predictive models which also maintain the parsimony and accuracy of models based on solely predictive approaches. This significantly enhances the opportunity for feedback among causal theory, empirical data and prediction within environmental modelling.     

Agro-ecosystem modeling can aid in the optimization of biomass feedstock supply

Recent European Directives promoted the development of biofuels, requesting mandatory limits to their emissions ot greenhouse gases (GHG). Second-generation biofuels based on lignocellulosic biomass are prime candidates but their GHG emissions are variable and uncertain. Agro-ecosystem modeling can capture them and the performance of biofuel feedstocks.This study aimed at optimizing feedstock supply for a bioethanol unit in France, from agricultural residues, annual and perennial crops. Their productivity and environmental impacts were modelled on a regional scale using geo-referenced data on soil properties, crop management, land-use and future weather data. Several supply scenarios were tested. Cereal straw was the most efficient feedstock but had a low availability, and only miscanthus could meet the bioethanol plant's demand. Sorghum combined poor yields and high GHG emissions compared by miscanthus and triticale. A mix of three biomass sources used less than 3% of the regional agricultural land while abating GHG emissions by 60%.     

Reusability of model components for environmental simulation – Case studies for integrated coastal zone management

Model constructs in environmental models are seldom reused beyond the project lifetime or in other modelling studies. A library of reusable model components could facilitate the maintenance of existing models and make the design of new models more efficient. Although component-based design is the common standard in software engineering and manufacturing few examples are yet found in environmental science. The multi-disciplinary project SPICOSA used a common, component-based simulation framework for environmental modelling, based on 18 case studies through Europe. The development of high-quality model components with potential for reuse turned out to be a challenge despite of the guidelines and tutorial examples provided. Well-designed components are of appropriate granularity, encapsulated, with a limited use of connectors and proper data handling. Ultimately, the success of a model library depends on a sufficient set of quality components with complementary functionalities, a framework for quality control, and support of the environmental modelling community.     

Iterative augmentation of a medical training simulator: Effects of affective metacognitive scaffolding

Experiential training simulators are gaining increasing popularity for job-related training due to their potential to engage and motivate adult learners. They are designed to provide learning experiences that are directly connected to users' work environments and support self-regulated learning. Nevertheless, learners often fail to transfer the knowledge gained in the simulated environment to real-world contexts. The EU-funded ImREAL project aimed to bridge that gap by developing a suite of intelligent services designed to enhance existing training simulators. This paper presents work that was a subset of this research project, reporting the iterative development and evaluation of a scaffolding service, which was integrated into a simulator for training medical students to perform diagnostic interviews. The study comprises three evaluation phases, comparing the pure simulator to a first version with metacognitive scaffolding and then to a final version with affective metacognitive scaffolding and enriched user modelling. The scaffolding service provides the learner with metacognitive prompts; affective elements are realized by an integrated affect reporting tool and affective prompts. Using a mixed-method approach by analysing questionnaires (N = 106) and log-data (N = 426), the effects of the services were investigated with respect to real-world relevance, self-regulated learning support, learning experience, and integration. Despite some limitations, the outcomes of this study demonstrate the usefulness of affective metacognitive scaffolding in the context of experiential training simulators; significant post-simulation increases in perceived relevance of the simulator, reflective note-taking, overall motivation, and feeling of success could be identified. Perceived usability and flow of the simulation increased, whereas overall workload and frustration decreased. However, low response rates to specific functions of the simulation point to a need to further investigate how to raise users' awareness and understanding of the provided tools, to encourage interaction with the services, and to better convey the benefits of using them. Thus, future challenges concern not so much technological developments for personalizing learning experiences, but rather new ways to change user attitudes towards an open approach to learning systems that enables them to benefit from all offered features.     

Impact of Internet gambling on problem gambling among adolescents in Italy: Findings from a large-scale nationally representative survey

AimsThe primary aim of the present study was to understand the impact of online gambling on gambling problems in a large-scale nationally representative sample of Italian youth, and to identify and then further examine a subgroup of online gamblers who reported higher rates of gambling problems.DesignData from the ESPAD^®Italia2013 (European School Survey Project on Alcohol and Other Drugs) Study were used for analyses of adolescent Internet gambling.SettingSelf-administered questionnaires were completed by a representative sample of high school students, aged 15–19 years.ParticipantsA total of 14,778 adolescent students.MeasurementsRespondents’ problem gambling severity; gambling behavior (participation in eight different gambling activities, the number of gambling occasions and the number of online gambling occasions, monthly gambling expenditure); Socio-demographics (e.g., family structure and financial status); and control variables were measured individually (i.e., use of the Internet for leisure activities and playing video games).FindingsRates of problem gambling were five times higher among online gamblers than non-online gamblers. In addition, factors that increased the risk of becoming a problem online gambler included living with non-birth parents, having a higher perception of financial family status, being more involved with gambling, and the medium preferences of remote gamblers (e.g., Internet cafes, digital television, and video game console).ConclusionsThe online gambling environment may pose significantly greater risk to vulnerable players. Family characteristics and contextual elements concerning youth Internet gambling (e.g., remote mediums) may play a key role in explaining problem online gambling among adolescents.     

Enablers and barriers to the use of ICT in primary schools in Turkey: A comparative study of 2005–2011

The purpose of this study was to reveal barriers encountered by Turkish primary school teachers in the integration of ICT, to propose potential enablers to overcome those barriers, and to compare the current status of ICT integration (in 2011) with the status of ICT integration in 2005. Part of the data for this comparison was gathered in 2005 as part of a doctoral study by Goktas (2006). A survey design was used to investigate the barriers and enablers. Data were collected from 1373 teachers from 52 schools in 39 provinces. The results indicate that ‘lack of hardware’, ‘lack of appropriate software materials’, ‘limitations of hardware’, ‘lack of in-service training’, and ‘lack of technical support’ were the most important barriers. The highest ranked enablers were ‘allocation of more budget’, ‘allocation of specific units for peer support’, ‘allocation of support offices and personnel for teachers’, and ‘offering higher quality pre-service training for ICT’. Other leading enablers were ‘supporting teachers to enable effective ICT use’, ‘having technology plans’, ‘offering higher quality and more quantity of in-service training’, and ‘designing appropriate course content/instructional programs’. Analysis of an independent t-test revealed that most barriers showed significant differences and most enablers showed moderate or low differences between teachers' perceptions of their situation in 2005 and in 2011.     

An extended systematic literature review on provision of evidence for safety certification

ContextCritical systems in domains such as aviation, railway, and automotive are often subject to a formal process of safety certification. The goal of this process is to ensure that these systems will operate safely without posing undue risks to the user, the public, or the environment. Safety is typically ensured via complying with safety standards. Demonstrating compliance to these standards involves providing evidence to show that the safety criteria of the standards are met.ObjectiveIn order to cope with the complexity of large critical systems and subsequently the plethora of evidence information required for achieving compliance, safety professionals need in-depth knowledge to assist them in classifying different types of evidence, and in structuring and assessing the evidence. This paper is a step towards developing such a body of knowledge that is derived from a large-scale empirically rigorous literature review.MethodWe use a Systematic Literature Review (SLR) as the basis for our work. The SLR builds on 218 peer-reviewed studies, selected through a multi-stage process, from 4963 studies published between 1990 and 2012.ResultsWe develop a taxonomy that classifies the information and artefacts considered as evidence for safety. We review the existing techniques for safety evidence structuring and assessment, and further study the relevant challenges that have been the target of investigation in the academic literature. We analyse commonalities in the results among different application domains and discuss implications of the results for both research and practice.ConclusionThe paper is, to our knowledge, the largest existing study on the topic of safety evidence. The results are particularly relevant to practitioners seeking a better grasp on evidence requirements as well as to researchers in the area of system safety. As a major finding of the review, the results strongly suggest the need for more practitioner-oriented and industry-driven empirical studies in the area of safety certification.     

Ergonomic evaluation of an alternative tool for cake decorating

AimCake decorating involves several hand intensive steps with high grip force during the application of icing. The purpose of this laboratory study was to evaluate forearm muscle activity, discomfort, productivity, and usability of an alternative tool for cake decorating compared to decorating with the traditional piping bag.MethodsParticipants (n = 17) performed 2 h of cake decorating tasks using the two tools. Subjective hand and arm fatigue, usability, upper extremity posture, and muscle activity from three forearm muscles were assessed for each tool. Outcome measures were evaluated using the Wilcoxon Signed Rank test and the paired t-test.ResultsLess fatigue was reported in the dominant hand (p = 0.001), forearm (p = 0.003) and shoulder (p = 0.02) for the alternative tool when compared to the piping bag. Average median (APDF 50%) and peak (APDF 90%) muscle activity was significantly less for the alternative tool across all three forearm muscles. The alternative tool significantly reduced grip force, an important risk factor for distal upper extremity pain and disorders. Participants rated usability of the alternative tool superior for refill and comfort but the traditional method was rated better for accuracy, stability, positioning and control.ConclusionsThe alternative tool was associated with less dominant arm fatigue, muscle activity, and grip force when compared with the piping bag. However, the alternative tool did not receive the best overall usability rating due to problems with accuracy and overflow, especially with smaller decorating tips. Recommendations were made for addressing these problems with the alternative tool.     

A spatially distributed risk screening tool to assess climate and land use change impacts on water-related ecosystem services

To support the implementation of the European Water Framework Directive (WFD), and as part of a tiered approach to prioritise detailed modelling, a high-level screening methodology has been developed to assess the vulnerability of water-related ecosystem services (ES) to future change. The approach incorporates a range of spatially distributed scenarios of land use and climate, which are used as inputs to a qualitative risk assessment model underpinned by expert opinion. The method makes use of widely available datasets and provides a structured way of capturing and “codifying” expert knowledge, as well as for assessing the degree of consensus between different expert groups. The range of model output reflects uncertainty in both the expert-derived assumptions and the climate & land use simulations considered. The approach has been developed in collaboration with the Scottish Environment Protection Agency (SEPA) and applied in Scotland to support the second cycle of River Basin Management Planning.     

Measuring the relation between computer use and reading literacy in the presence of endogeneity

This work studies the relation between computer use for reading activities and academic literacy in 15-year-old students in Chile, Uruguay, Spain, and Portugal. Data used is from the PISA 2009 test. Special attention is given to potential bias problems when the computer use is an endogenous variable. Few studies in this area address this issue: existing literature has shown that different types of computer use have different implications on performance. The limitations of observational data have also been emphasized to establish cause–effect relations between computer use and academic performance. It is important, however, to consider the computer use endogeneity hypothesis (above all at home) since students decide on the frequency of computer use at home. The results found show that by controlling for endogeneity, computer use for reading is not related to reading performance neither in digital or printed format, with the exception of Chile that shows a negative relation in the case of reading from a printed format. The results considering endogeneity differ considerably from results when endogeneity is not taken into account. The work shows the relevance of experimental type studies in order to make sound statements with regard to the computer use and academic performance relation. In turn, school reading activities in a digital environment are suggested that could have an impact on reading performance.     

Posture and lifting exposures for daycare workers

Daycare employees, specifically caregivers, are a distinct population that may experience increased risk of injury due to the high exposure to bent postures, lifting conditions and high stress associated with their work. The objectives of the study were to collect up to date data on daycare workers and to compare the data between groups working with children of different ages (Infant, Toddler and Preschool). The study consisted of two distinct phases: Phase 1 – Questionnaire distribution, Phase 2 – Observation and analysis involving three dimensional postural monitoring and video recording as well as an analysis of the low back forces and moments in lifting. Phase 1: Consisted of the distribution of questionnaires to all employees in each of the participating daycares (n = 73). Of the 73 questionnaires distributed 32 responses were obtained (44%). Of the 32 employees who completed the questionnaires, 19 caregivers volunteered to participate in Phase 2 of the study. An additional 5 caregivers participated in phase 2 of the study, but did not complete any questionnaires. The questionnaires indicated 81% of the workers have experienced low back pain. Phase 2: Observational data were collected on site in five local daycares, throughout the first half of each subject's shift (∼3.5 h). Caregivers from each of the three classroom age groups were recruited for participation in the direct observation (Infant: n = 7, Toddler: n = 7, Preschool: n = 8). Posture analysis revealed that on average, workers adopted trunk flexion angles greater than 55°, for 10% of the collection time, and greater than 70°, for 5% of the collection time. These postures correspond to both moderate and severe flexed postures respectively. The lifting analysis (completed using the data recorded in phase 2) revealed that workers lifted with frequencies of 0.25 lifts/minute, lifted a total weight of 501 kg (over 3.3 h) and experienced average compression and shear forces of 3323 N and 371 N, respectively. A between-group comparison showed that when compared to the Preschool group, the Infant (p = 0.008) and Toddler (p = 0.001) groups demonstrated higher relative flexed postures and lifting frequencies, and the Toddler group (p = 0.023) demonstrated higher total weight lifted. Results suggested that these employees experience an elevated risk of low back injury caused by their occupational tasks and thus, further research is required to determine appropriate worker accommodations and safe work practices to help mediate these risks for all daycare caregivers.Relevance to industryIt is thought that the results from this study could lead to the development of safe working and job sharing guidelines for daycare workers.     

Use of freely available datasets and machine learning methods in predicting deforestation

The range and quality of freely available geo-referenced datasets is increasing. We evaluate the usefulness of free datasets for deforestation prediction by comparing generalised linear models and generalised linear mixed models (GLMMs) with a variety of machine learning models (Bayesian networks, artificial neural networks and Gaussian processes) across two study regions. Freely available datasets were able to generate plausible risk maps of deforestation using all techniques for study zones in both Mexico and Madagascar. Artificial neural networks outperformed GLMMs in the Madagascan (average AUC 0.83 vs 0.80), but not the Mexican study zone (average AUC 0.81 vs 0.89). In Mexico and Madagascar, Gaussian processes (average AUC 0.89, 0.85) and structured Bayesian networks (average AUC 0.88, 0.82) performed at least as well as GLMMs (average AUC 0.89, 0.80). Bayesian networks produced more stable results across different sampling methods. Gaussian processes performed well (average AUC 0.85) with fewer predictor variables.     

Unfriend or ignore tweets?: A time series analysis on Japanese Twitter users suffering from information overload

In recent years, with increased opportunities to post content on social media, a number of users are experiencing information overload in relation to social media use. This study addresses how Japanese Twitter users suffering from information overload cope with their stress, focusing on two actions: (1) The “unfriending” activities and (2) The changes in tweet processing methods. Objective data, such as numbers of friends, were collected through Twitter's open Application Programming Interfaces (APIs), and subjective data, such as perceived information overload and tweet processing methods, were collected through a web-based survey as a panel dataset (n = 778). The results demonstrated that although users experience information overload, they continue to increase their number of friends, and that the users who experience information overload modify their usage habits to avoid seeing all received tweets. In short, users do not choose a strategy to reduce the absolute number of received tweets, but only a strategy that involves changing the processing method of the received tweets.     

Utilizing slope method as an alternative data analysis for functional near-infrared spectroscopy-derived cerebral hemodynamic responses

The purpose was to propose an alternative data analysis for functional near-infrared spectroscopy (fNIRS)-derived hemodynamics as a function of cortical activation changes. We evaluated hemodynamic responses from the prefrontal cortex region while 38 participants performed a cognitive task. The task consisted of an arithmetic calculation with three levels of complexity (i.e., easy, medium and difficult). These task-dependent hemodynamic responses were analyzed by the slope method (i.e. using a linear regression through the cognitive task) and were compared with the corresponding responses obtained with a traditional approach of the amplitude method. Subjective scales of task loading (assessed by DP15 and NASA-TLX) and behavioral outcomes (performance and reaction time) were also recorded in response to the task complexity. Results revealed that the proposed slope method allowed a better discrimination in terms of cortical activation among all levels of mental workload. There was no significant increase in cortical activation between the medium and difficult levels (p = .30; d = 0.09) with the amplitude method while the slope coefficient was sensitive to the different levels (p < .01; d = 0.32). These preliminary results from a large sample size demonstrated that the slope method appears suitable for discriminating the changes in cortical activation with respect to the mental workload.Relevance to industryIn this work, we proposed an optimum way of quantifying the mental workload of participants in terms of fNIRS-derived cerebral hemodynamic responses.     

Defining limits to multiple and simultaneous anthropogenic stressors in a lake ecosystem – Lake Kinneret as a case study

In this study we expanded a recently developed approach for defining acceptable levels of management policy that will allow sustainable management of water quality in a lake ecosystem. A three dimensional solution space was created to define all acceptable scenarios of N loads, P loads and lake water level (WL) thus providing an integrated tool for defining the extent of measures that will allow lake ecosystem sustainability. The approach included use of a lake ecosystem model, a quantitative system of composite water quality indices (CWQIs) and defined sustainability criteria for the ecosystem. The approach was tested on the Lake Kinneret (Sea of Galilee) ecosystem and succeeded in defining the range of acceptable management policy through the use of long term simulations of different scenarios. Using the results of the scenarios, a number of polygons were created, defined as relative solution domain area (RSDA), which denote the permissible ranges of nutrient loads at different water levels. The polygon, and hence RSDA, boundaries represent critical values of nutrient loads allowing conservation of the lake water quality at each WL. By integrating all RSDA, a three dimensional solution space was created which defines all acceptable ranges of N loads, P loads and WL thus providing lake managers with an integrated tool for defining the extent of measures that will allow sustainability of the lake ecosystem. This novel approach is unique, and presents an example of implementation of a management tool that integrates an ecosystem model, multiple stressors and quantified water quality indices to determine limits of management actions. This approach may well be implemented to other lakes around the world suffering from water quality deterioration as a result of changes in water level and nutrients loads.     

Activity theory as a framework for building adaptive e-learning systems: A case to provide empirical evidence

We apply activity theory (AT) to design adaptive e-learning systems (AeLS). AT is a framework to study human’s behavior at learning; whereas, AeLS enhance students’ apprenticeship by the personalization of teaching–learning experiences. AeLS depict users’ traits and predicts learning outcomes. The approach was successfully tested: Experimental group took lectures chosen by the anticipation AT principle; whilst, control group received randomly selected lectures. Learning achieved by experimental group reveals a correlation quite significant and high positive; but, for control group the correlation it is not significant and medium positive. We conclude: AT is a useful framework to design AeLS and provide student-centered education.