Global parameter optimization for biokinetic modeling of simple batch experiments

Environmental process modeling is challenged by the lack of high quality data, stochastic variations, and nonlinear behavior. Conventionally, parameter optimization is based on stochastic sampling techniques to deal with the nonlinear behavior of the proposed models. Despite widespread use, such tools cannot guarantee globally optimal parameter estimates. It can be especially difficult in practice to differentiate between lack of algorithm convergence, convergence to a non-global local optimum, and model structure deficits. For this reason, we use a deterministic global optimization algorithm for kinetic model identification and demonstrate it with a model describing a typical batch experiment. A combination of interval arithmetic, reformulations, and relaxations allows globally optimal identification of all (six) model parameters. In addition, the results suggest that further improvements may be obtained by modification of the optimization problem or by proof of the hypothesized pseudo-convex nature of the problem suggested by our results.     

Software framework for inverse modeling and uncertainty characterization

Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing SRFs, which is an implementation of the Bayesian inverse modeling technique Method of Anchored Distributions (MAD). MAD# allows modelers to “wrap” simulation models using an extensible driver architecture that exposes model parameters to the inversion engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration, external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters characterizing a groundwater aquifer.     

Ubiquitin: Molecular modeling and simulations

The synthesis and destruction of proteins are imperative for maintaining their cellular homeostasis. In the 1970s, Aaron Ciechanover, Avram Hershko, and Irwin Rose discovered that certain proteins are tagged by ubiquitin before degradation, a discovery that awarded them the 2004 Nobel Prize in Chemistry. Compelling data gathered during the last several decades show that ubiquitin plays a vital role not only in protein degradation but also in many cellular functions including DNA repair processes, cell cycle regulation, cell growth, immune system functionality, hormone-mediated signaling in plants, vesicular trafficking pathways, regulation of histone modification and viral budding. Due to the involvement of ubiquitin in such a large number of diverse cellular processes, flaws and impairments in the ubiquitin system were found to be linked to cancer, neurodegenerative diseases, genetic disorders, and immunological disorders. Hence, deciphering the dynamics and complexity of the ubiquitin system is of significant importance. In addition to experimental techniques, computational methodologies have been gaining increasing influence in protein research and are used to uncover the structure, stability, folding, mechanism of action and interactions of proteins. Notably, molecular modeling and molecular dynamics simulations have become powerful tools that bridge the gap between structure and function while providing dynamic insights and illustrating essential mechanistic characteristics. In this study, we present an overview of molecular modeling and simulations of ubiquitin and the ubiquitin system, evaluate the status of the field, and offer our perspective on future progress in this area of research.     

Effect of handle design on movement dynamics and muscle co-activation in a wrist flexion task

Arm and wrist manipulanda are commonly used as input devices in teleoperation and gaming applications, establish a physical interface to patients in several rehabilitation robots, and are applied as advanced research tools in biomechanics and neuroscience. Despite the fact that the physical interface, i.e. the handle through which the wrist/hand is attached to the manipulator, may influence interaction and movement behavior, the effects of handle design on these parameters has received little attention. Yet, a poor handle design might lead to overexertion and altered movement dynamics, or result in misinterpretation of results in research studies. In this study, twelve healthy subjects performed repetitions of a wrist flexion task against a dynamic load generated by a 1-DOF robotic wrist manipulandum. Three different handle designs were qualitatively and quantitatively evaluated based on wrist movement kinematics and dynamics, patterns of finger and wrist muscle activity, and ergonomics criteria such as perceived comfort and fatigue. The three proposed designs were further compared to a conventional joystick-like handle. Task performance as well as kinematic and kinetic parameters were found to be unaffected by handle design. Nevertheless, differences were found in perceived task difficulty, comfort and levels of muscle activation of wrist and finger muscles, with significantly higher muscle activation when using a joystick-like design, where the handle is completely enclosed by the hand. Comfort was rated high for the flat handle, adapted to the natural curvature of the hand with the fingers extended. These results may inform for the design of handles serving as physical interface in teleoperation applications, robot-assisted rehabilitation and biomechanics/neuroscience research.     

A new method for trust and reputation evaluation in the cloud environments using the recommendations of opinion leaders' entities and removing the effect of troll entities

Trust in the cloud environment is not written into an agreement and is something earned. In any trust evaluation mechanism, opinion leaders are the entities influencing the behaviors or attitudes of others, this makes them to be trustworthy, valid among other characteristics. On the other hand, trolls are the entities posting incorrect and unreal comments; therefore, their effect must be removed. This paper evaluates the trust by considering the influence of opinion leaders on other entities and removing the troll entities’ effect in the cloud environment. Trust value is evaluated using five parameters; availability, reliability, data integrity, identity and capability. Also, we propose a method for opinion leaders and troll entity identification using three topological metrics, including input-degree, output-degree and reputation measures. The method being evaluated in various situation where shows the results of accuracy by removing the effect of troll entities and the advice of opinion leaders.     

Integrated watershed modeling for simulation of spatiotemporal redistribution of post-fallout radionuclides: Application in radiocesium fate and transport processes derived from the Fukushima accidents

Simulation of the watershed-scale fate and transport of radionuclides is required in order to predict the consequences of contamination redistribution. Integrated watershed modeling is a suitable technique for this task, but it requires fully coupled investigation of radionuclide behavior in surface water, suspended sediment and subsurface aquifers. We developed a novel simulator for computing the spatiotemporal redistribution of fallout radionuclides in watersheds. The simulator was applied to an actual reservoir basin contaminated by fallout radionuclides from the Fukushima Dai-Ichi Nuclear Power Plant accident in 2011. As a result, the simulated ¹³⁷Cs concentration in bottom sediment showed a reasonably close match with the measurement data. The distribution coefficient of ¹³⁷Cs consistent with the latest measurement data was identified as being at least 400,000 L/kg, and it was estimated that more than 90% of the total ¹³⁷Cs distributed in the fallout remains in the catchment area.     

Simulating agricultural land rental markets by combining agent-based models with traditional economics concepts: The case of the Argentine Pampas

Land exchange through rental transactions is a central process in agricultural systems. The land tenure regimes emerge from land transactions and structural and land use changes are tied to the dynamics of the land market. We introduce LARMA, a LAnd Rental MArket model embedded within the Pampas Model (PM), an agent-based model of Argentinean agricultural systems. LARMA produces endogenous formation of land rental prices. LARMA relies on traditional economic concepts for LRP formation but addresses some drawbacks of this approach by being integrated into an agent-based model that considers heterogeneous agents interacting with one another. PM-LARMA successfully reproduced the agricultural land tenure regimes and land rental prices observed in the Pampas. Including adaptive, heterogeneous and interacting agents was critical to this success. We conclude that agent-based and traditional economic models can be successfully combined to capture complex emergent land tenure and market price patterns while simplifying the overall model design.     

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.     

Genetic based discrete particle swarm optimization for Elderly Day Care Center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency.     

Individual-based modeling of soil organic matter in NetLogo: Transparent,user-friendly,and open

Soil organic matter dynamics are essential for terrestrial ecosystem functions as they affect biogeochemical cycles and, thus, the provision of plant nutrients or the release of greenhouse gases to the atmosphere. Most of the involved processes are driven by microorganisms. To investigate and understand these processes, individual-based models allow analyzing complex microbial systems' behavior based on rules and conditions for individual entities within these systems, taking into account local interactions and individual variations. Here, we present a streamlined, user-friendly and open version of the individual-based model INDISIM-SOM, which describes the mineralization of soil carbon and nitrogen. It was implemented in NetLogo, a widely used and easily accessible software platform especially designed for individual-based simulation models. Including powerful means to observe the model behavior and a standardized documentation, this increases INDISIM-SOM's range of potential uses and users, and facilitates the exchange among soil scientists as well as between different modeling approaches.     

A methodological approach to the design of optimising control strategies for sewer systems

This study focuses on designing an optimisation based control for sewer system in a methodological way and linking it to a regulatory control. Optimisation based design is found to depend on proper choice of a model, formulation of objective function and tuning of optimisation parameters. Accordingly, two novel optimisation configurations are developed, where the optimisation either acts on the actuators or acts on the regulatory control layer. These two optimisation designs are evaluated on a sub-catchment of the sewer system in Copenhagen, and found to perform better than the existing control; a rule based expert system. On the other hand, compared with a regulatory control technique designed earlier in Mollerup et al. (2015), the optimisation showed similar performance with respect to minimising overflow volume. Hence for operation of small sewer systems, regulatory control strategies can offer promising potential and should be considered along more advanced strategies when identifying novel solutions.     

Effect of body-borne equipment on injury of military pilots and aircrew during a simulated helicopter crash

Military helicopter pilots are expected to wear a variety of items of body-borne equipment during flight so as to be prepared for any situation that may arise in combat. Helicopter seats are designed to a specified weight range for an occupant with equipment. This paper investigates how distributing the equipment on the body affects injury potential during a helicopter crash. A finite element model representing a helicopter seat with a fully deformable 50th percentile Hybrid III carrying equipment was developed. The model was subjected to a standard military certification crash test. Various equipment configurations were investigated and analysed to determine its influence on the risk of injury. It was found that placing the equipment low on the torso, i.e. near the thighs, not only reduces the likelihood of injury in the lumbar, spinal region but also provides favourable results in neck and head injury risk when compared to other configurations investigated. In contrast, placing equipment high on the torso, i.e. close to the chin, increases the lumbar load and implicitly, the risk of head injury. A statistical analysis is carried out using the Wilcoxon Signed Rank Test to deliver probability of loads experienced within a certain interval. This study recommends an equipment configuration that improves survivability for an occupant seated on a fixed load energy absorbing seat which is subjected to Military Standard 58095A Test 4.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.     

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.     

Prevalence and personality correlates of Facebook bullying among university undergraduates

The purpose of the present study was to examine the prevalence of cyber-bullying through Facebook in a sample of 226 Greek university undergraduates, and to explore whether big five personality characteristics, narcissism, as well as attitudes toward Facebook, technological knowledge and skills were predictive of such behavior. Participants completed a self-report questionnaire measuring the above constructs. Results indicated that almost one third of the sample reported Facebook bullying engagement at least once during the past month, with male students reporting more frequent involvement than females. Bullying through Facebook was predicted by low Agreeableness and more time spent on Facebook only for males, whereas for females none of the studied variables predicted engagement in Facebook bullying. Findings are discussed in terms of prevention and intervention strategies.     

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.     

Development of predictive models for the estimation of the probability of suffering fear of falling and other fall risk factors based on posturography parameters in community-dwelling older adults

Falls pose an important problem for older adults. Balance training is one of the main prevention strategies, but there is a lack of objective measurement methods that would allow the effectiveness of the treatments employed to be assessed. This study aimed to analyse the relationship between posturographic parameters and risk factors associated with falling, including the fear of falling (FoF). Forty-one healthy community-dwelling older adults were surveyed on their perception of problems considered to be fall risk factors. Balance measurement with posturography was performed. The relationships between risk factors and falls and risk factors and posturography were analysed by means of cross-tabulation and logistic regression, respectively. Experimental results showed a significant relationship between some of the posturographic parameters and various fall risk factors. Stability limits were related to FoF, and results from the Romberg test with eyes closed with and without foam correlated with problems in kneeling/crouching. The results from the Romberg test with eyes closed and foam correlated with osteoarthritis. Equations were developed to estimate the probability of having such problems. In conclusion, posturography is useful for the estimation of fall risk conditions in relation to three important fall risk factors (FoF, osteoarthritis and problems in kneeling/crouching), and it could be used for targeting, training and studying progress after the use of different treatments.Relevance to industryPosturography can be used as an assessment tool to analyse the effects of those treatments aimed at preventing falls. Furthermore, the equations derived from our results can be used along with posturographic variables to assess patient progress.     

Electric vehicles charging control in a smart grid: A model predictive control approach

The paper presents an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid. The objective is to minimize the cost of energy consumption, while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator). The proposed control approach allows “flexible” EV users to participate in demand side management (DSM) programs, which will play a crucial role in improving stability and efficiency of future smart grids. Further, the natural MPC formulation of the problem can be recast into a mixed integer linear programming problem, suitable for implementation on a calculator. Simulation results are provided and discussed in detail.     

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.