Objective analysis of contact lens fit

Purpose: To assess the validity and repeatability of objective compared to subjective contact lens fit analysis. Methods: Thirty-five subjects (aged 22.0. ±. 3.0 years) wore two different soft contact lens designs. Four lens fit variables: centration, horizontal lag, post-blink movement in up-gaze and push-up recovery speed were assessed subjectively (four observers) and objectively from slit-lamp biomicroscopy captured images and video. The analysis was repeated a week later. Results: The average of the four experienced observers was compared to objective measures, but centration, movement on blink, lag and push-up recovery speed all varied significantly between them (p <. 0.001). Horizontal lens centration was on average close to central as assessed both objectively and subjectively (p > 0.05). The 95% confidence interval of subjective repeatability was better than objective assessment (±0.128. mm versus ±0.168. mm, p = 0.417), but utilised only 78% of the objective range. Vertical centration assessed objectively showed a slight inferior decentration (0.371. ±. 0.381. mm) with good inter- and intrasession repeatability (p > 0.05). Movement-on-blink was lower estimated subjectively than measured objectively (0.269. ±. 0.179. mm versus 0.352. ±. 0.355. mm; p = 0.035), but had better repeatability (±0.124. mm versus ±0.314. mm 95% confidence interval) unless correcting for the smaller range (47%). Horizontal lag was lower estimated subjectively (0.562. ±. 0.259. mm) than measured objectively (0.708. ±. 0.374. mm, p <. 0.001), had poorer repeatability (±0.132. mm versus ±0.089. mm 95% confidence interval) and had a smaller range (63%). Subjective categorisation of push-up speed of recovery showed reasonable differentiation relative to objective measurement (p <. 0.001). Conclusions: The objective image analysis allows an accurate, reliable and repeatable assessment of soft contact lens fit characteristics, being a useful tool for research and optimisation of lens fit in clinical practice.

A Knowledge-Oriented Technology of System-Objective Analysis and Modelling of Business-Systems

A new original method and CASE-tool of system analysis and modelling are represented. They are for the first time consistent with the requirements of object-oriented technology of informational systems design. They essentially facilitate the construction of organisational systems models and increase the quality of the organisational designing and basic technological processes of object application developing.

Temperature management for heterogeneous multi-core FPGAs using adaptive evolutionary multi-objective approaches

Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.

Evidence of the heterogeneous market hypothesis using wavelet multi -resolution analysis

In finance literature many economic theories and models have been proposed to explain and estimate the relationship between risk and return. Assuming risk averseness and rational behavior on part of the investor, the models are developed which are supposed to help in forming efficient portfolios that either maximize (minimize) the expected rate of return (risk) for a given level of risk (rates of return). One of the most used models to form these efficient portfolios is the Sharpe's Capital Asset Pricing Model (CAPM). In the development of this model it is assumed that the investors have homogeneous expectations about the future probability distribution of the rates of return. That is, every investor assumes the same values of the parameters of the probability distribution. Likewise financial volatility homogeneity is commonly assumed, where volatility is taken as investment risk which is usually measured by the variance of the rates of return. Typically the square root of the variance is used to define financial volatility, furthermore it is also often assumed that the data generating process is made of independent and identically distributed random variables. This again implies that financial volatility is measured from homogeneous time series with stationary parameters. In this dissertation, we investigate the assumptions of homogeneity of market agents and provide evidence for the case of heterogeneity in market participants' information, objectives, and expectations about the parameters of the probability distribution of prices as given by the differences in the empirical distributions corresponding to different time scales, which in this study are associated with different classes of investors, as well as demonstrate that statistical properties of the underlying data generating processes including the volatility in the rates of return are quite heterogeneous. In other words, we provide empirical evidence against the traditional views about homogeneity using non-parametric wavelet analysis on trading data, The results show heterogeneity of financial volatility at different time scales, and time-scale is one of the most important aspects in which trading behavior differs. In fact we conclude that heterogeneity as posited by the Heterogeneous Markets Hypothesis is the norm and not the exception.

Multi-objective optimization of the molecular structure of refrigerants

The aim of this work was to develop a new methodology, which can be used to design new refrigerants that are better than the currently used refrigerants. The methodology draws some parallels with the general approach of computer aided molecular design. However, the mathematical way of representing the molecular structure of an organic compound and the use of meta models during the optimization process make it different. In essence, this approach aimed to generate molecules that conform to various property requirements that are known and specified a priori. A modified way of mathematically representing the molecular structure of an organic compound having up to four carbon atoms, along with atoms of other elements such as hydrogen, oxygen, fluorine, chlorine and bromine, was developed. The normal boiling temperature, enthalpy of vaporization, vapor pressure, tropospheric lifetime and biodegradability of 295 different organic compounds, were collected from open literature and data bases or estimated. Surrogate models linking the previously mentioned quantities with the molecular structure were developed. Constraints ensuring the generation of structurally feasible molecules were formulated and used in commercially available optimization algorithms to generate molecular structures of promising new refrigerants. This study was intended to serve as a proof-of-concept of designing refrigerants using the newly developed methodology.

Structural damage estimation using dynamic data and multi-objective optimization

Peer reviewed

Evaluating a School Community Linked Physical Activity Intervention Programme: a Multi-level Analysis.

Previous research has highlighted the importance of positive physical activity (PA) behaviors during childhood to promote sustained active lifestyles throughout the lifespan (Telama et al. 2005; 2014). It is in this context that the role of schools and teachers in facilitating PA education is promoted. Research suggests that teachers play an important role in the attitudes of children towards PA (Figley 1985) and schools may be an efficient vehicle for PA provision and promotion (McGinnis, Kanner and DeGraw, 1991; Wechsler, Deveraux, Davis and Collins, 2000). Yet despite consensus that schools represent an ideal setting from which to ‘reach’ young people (Department of Health and Human Services, UK, 2012) there remains conceptual (e.g. multi-component intervention) and methodological (e.g. duration, intensity, family involvement) ambiguity regarding the mechanisms of change claimed by PA intervention programmes. This may, in part, contribute to research findings that suggest that PA interventions have had limited impact on children’s overall activity levels and thereby limited impact in reducing children’s metabolic health (Metcalf, Henley & Wilkin, 2012). A marked criticism of the health promotion field has been the focus on behavioural change while failing to acknowledge the impact of context in influencing health outcomes (Golden & Earp, 2011). For years, the trans-theoretical model of behaviour change has been ‘the dominant model for health behaviour change’ (Armitage, 2009); this model focusses primarily on the individual and the psychology of the change process. Arguably, this model is limited by the individual’s decision-making ability and degree of self-efficacy in order to achieve sustained behavioural change and does not take account of external factors that may hinder their ability to realise change. Similar to the trans-theoretical model, socio-ecological models identify the individual at the focal point of change but also emphasises the importance of connecting multiple impacting variables, in particular, the connections between the social environment, the physical environment and public policy in facilitating behavioural change (REF). In this research, a social-ecological framework was used to connect the ways a PA intervention programme had an impact (or not) on participants, and to make explicit the foundational features of the programme that facilitated positive change. In this study, we examined the evaluation of a multi-agency approach to a PA intervention programme which aimed to increase physical activity, and awareness of the importance of physical activity to key stage 2 (age 7-12) pupils in three UK primary schools. The agencies involved were the local health authority, a community based charitable organisation, a local health administrative agency, and the city school district. In examining the impact of the intervention, we adopted a process evaluation model in order to better understand the mechanisms and context that facilitated change. Therefore, the aim of this evaluation was to describe the provision, process and impact of the intervention by 1) assessing changes in physical activity levels 2) assessing changes in the student’s attitudes towards physical activity, 3) examining student’s perceptions of the child size fitness equipment in school and their likelihood of using the equipment outside of school and 4) exploring staff perceptions, specifically the challenges and benefits, of facilitating equipment based exercise sessions in the school environment. Methodology, Methods, Research Instruments or Sources Used Evaluation of the intervention was designed as a matched-control study and was undertaken over a seven-month period. The school-based intervention involved 3 intervention schools (n =436; 224 boys) and one control school (n=123; 70 boys) in a low socioeconomic and multicultural urban setting. The PA intervention was separated into two phases: a motivation DVD and 10 days of circuit based exercise sessions (Phase 1) followed by a maintenance phase (Phase 2) that incorporated a PA reward program and the use of specialist kid’s gym equipment located at each school for a period of 4 wk. Outcome measures were measured at baseline (January) and endpoint (July; end of academic school year) using reliable and valid self-report measures. The children’s attitudes towards PA were assessed using the Children’s Attitudes towards Physical Activity (CATPA) questionnaire. The Physical Activity Questionnaire for Children (PAQ-C), a 7-day recall questionnaire, was used to assess PA levels over a school week. A standardised test battery (Fitnessgram®) was used to assess cardiovascular fitness, body composition, muscular strength and endurance, and flexibility. After the 4 wk period, similar kid’s equipment was available for general access at local community facilities. The control school did not receive any of the interventions. All physical fitness tests and PA questionnaires were administered and collected prior to the start of the intervention (January) and following the intervention period (July) by an independent evaluation team. Evaluation testing took place at the individual schools over 2-3 consecutive days (depending on the number of children to be tested at the school). Staff (n=19) and student perceptions (n = 436) of the child sized fitness equipment were assessed via questionnaires post-intervention. Students completed a questionnaire to assess enjoyment, usage, ease of use and equipment assess and usage in the community. A questionnaire assessed staff perceptions on the delivery of the exercise sessions, classroom engagement and student perceptions. Conclusions, Expected Outcomes or Findings Findings showed that both the intervention (16.4%) and control groups increased their PAQ-C score by post-intervention (p < 0.05); with the intervention (17.8%) and control (21.3%) boys showing the greatest increase in physical activity levels. At post-intervention, there was a 5.5% decline in the intervention girls’ attitudes toward PA in the aesthetic subdomains (p = 0.009); whereas the control boys had an increase in positive attitudes in the health domain (p = 0.003). No significant differences in attitudes towards physical activity were observed in any other domain for either group at post-intervention (p > 0.05). The results of the equipment questionnaire, 96% of the children stated they enjoyed using the equipment and would like to use the equipment again in the future; however at post-intervention only 27% reported using the equipment outside of school in the last 7 days. Students identified the ski walker (34%) and cycle (32%) as their favorite pieces of equipment; with the single joint exercises such as leg extension and bicep/tricep machine (<3%) as their least favorite. Key themes from staff were that the equipment sessions were enjoyable, a novel activity, children felt very grown-up, and the activity was linked to a real fitness experience. They also expressed the need for more support to deliver the sessions and more time required for each session. Findings from this study suggest that a more integrated approach within the various agencies is required, particularly more support to increase teachers pedagogical content knowledge in physical activity instruction which is age appropriate. Future recommendations for successful implementation include sufficient time period for all students to access and engage with the equipment; increased access and marketing of facilities to parents within the local community, and professional teacher support strategies to facilitate the exercise sessions.

Multi-objective ant colony optimization for requirements selection

The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

Multi-residue analysis of 39 pesticides in surface waters of Ria de Aveiro

A multi-residue gas chromatography-mass spectrometry method was developed in order to evaluate the presence of 39 pesticides of different chemical families (organophosphorus, triazines, imidazole, organochlorine), as well as some of their transformation products, in surface water samples from Ria de Aveiro. Ria de Aveiro is an estuarine coastal lagoon, located in the northern west region of Portugal, which receives inputs from agriculture, urban and industrial activities. The analytical method was developed and validated according international guidelines and showed good linearity, with correlation coefficients higher than 0.9949 for all compounds, adequate precision and accuracy, and high sensitivity. Pesticides were chosen from the priority pollutants list of the Directive 2008/105/EC of the European Parliament and of the Council (on environmental quality standards in the field of water policy), or were selected due their common use in agricultural practices. Some of these 39 pesticides are, or are suspected to be, endocrine disruptor compounds (EDCs), being capable of altering the endocrine system of wildlife and humans, causing form malfunction and ultimately health problems. Even those pesticides which are not EDCs, are known to be awfully toxic and have a recognised impact in human health. The aquatic environment is particularly susceptible to pollution due to intentional and accidental release of chemicals to water [3]. Pesticide contamination of surface water is a national issue as it is often used as drinking water. This concern is especially important in rural agricultural areas where population uses small private water supplies, regularly without any laboratory surveillance. The study was performed in seven sampling points and the results showed a considerable concern pesticide contamination of all samples.

A Multi-Objective Approach for VNE: Problems using multiple ILP formulations

Network Virtualization is a key technology for the Future Internet, allowing the deployment of multiple independent virtual networks that use resources of the same basic infrastructure. An important challenge in the dynamic provision of virtual networks resides in the optimal allocation of physical resources (nodes and links) to requirements of virtual networks. This problem is known as Virtual Network Embedding (VNE). For the resolution of this problem, previous research has focused on designing algorithms based on the optimization of a single objective. On the contrary, in this work we present a multi-objective algorithm, called VNE-MO-ILP, for solving dynamic VNE problem, which calculates an approximation of the Pareto Front considering simultaneously resource utilization and load balancing. Experimental results show evidences that the proposed algorithm is better or at least comparable to a state-of-the-art algorithm. Two performance metrics were simultaneously evaluated: (i) Virtual Network Request Acceptance Ratio and (ii) Revenue/Cost Relation. The size of test networks used in the experiments shows that the proposed algorithm scales well in execution times, for networks of 84 nodes

Defining efficient policies in a general equilibrium model: a multi-objective approach

Macroeconomic policy makers are typically concerned with several indicators of economic performance. We thus propose to tackle the design of macroeconomic policy using Multicriteria Decision Making (MCDM) techniques. More specifically, we employ Multiobjective Programming (MP) to seek so-called efficient policies. The MP approach is combined with a computable general equilibrium (CGE) model. We chose use of a CGE model since they have the dual advantage of being consistent with standard economic theory while allowing one to measure the effect(s) of a specific policy with real data. Applying the proposed methodology to Spain (via the 1995 Social Accounting Matrix) we first quantified the trade-offs between two specific policy objectives: growth and inflation, when designing fiscal policy. We then constructed a frontier of efficient policies involving real growth and inflation. In doing so, we found that policy in 1995 Spain displayed some degree of inefficiency with respect to these two policy objectives. We then offer two sets of policy recommendations that, ostensibly, could have helped Spain at the time. The first deals with efficiency independent of the importance given to both growth and inflation by policy makers (we label this set: general policy recommendations). A second set depends on which policy objective is seen as more important by policy makers: increasing growth or controlling inflation (we label this one: objective-specific recommendations).

A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.

Dynamic Multi-Objective Optimization With jMetal and Spark: a Case Study

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark to feed an optimization problem with data from different sources. We demonstrate the use of our tool by solving a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on near real-time traffic data from New York City, which is updated several times per minute. Our experiment shows that both jMetal and Spark can be integrated providing a software platform to deal with dynamic multi-optimization problems.

Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2

The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.