Connecting the dots: Understanding the interrelated impacts of type,quality and children's awareness of design features and the mathematics content learning goals in digital math games and related learning outcomes

This study focused on an examination of how type, quality and children's awareness of design features in digital math games, along with an awareness of the mathematics goals of the game, were related to learning outcomes. We conducted a parallel conversion mixed methods study with 45 students in Grades 3 and 4 (ages 9–10). Students participated in clinical interviews using three digital math games. The results suggest a moderated mediation relationship in which the quality of the design features moderated the mediating impact of children's awareness of the game's design features, specifically when the child was aware of the mathematics content learning goal (MCLG) of the game. These findings show how important it is that design features are of high quality in a digital math game, and how this is intertwined with children's awareness of the features and the MCLG. When these variables intertwined in just the right way, the interactions between the children and the digital math game afforded mathematical learning growth.     

Friction and adsorption properties of normal and high-oleic soybean oils

The steel/steel boundary friction properties of soybean oil (SBO) and high-oleic soybean oil (HOSBO) are compared. HOSBO is significantly more saturated than SBO and more oxidatively stable. Changes in degree of unsaturation affect lateral interactions of adsorbate molecules, which in turn affects their adsorption and, hence, their boundary lubrication properties. To investigate this possibility, the free energies of adsorption (ΔG _ads) of SBO, HOSBO, and methyl laurate (ML) were determined from the analysis of friction-derived adsorption isotherms using the Langmuir and Temkin adsorption models. The results showed a stronger adsorption for the vegetable oils than for ML, an indication of multiple interactions between the ester groups of the triglycerides and the steel surface. The result also showed no difference in the ΔG _ads values of SBO and HOSBO obtained using either the Langmuir or Temkin models. This was interpreted as an indication of the lack of appreciable net lateral interaction between triglyceride adsorbates.     

Effects of composition and phase relations on mechanical properties and crystallization of silicate glasses

Crystallization, mechanical properties, and workability are all important for the commercialization and optimization of silicate glass compositions. However, the inter-relations of these properties as a function of glass composition have received little investigation. Soda-lime-silica glasses with Na₂O-MgO-CaO-Al₂O₃-SiO₂ compositions relevant to commercial glass manufacture were experimentally studied and multiple liquidus temperature and viscosity models were used to complement the experimental results. Liquidus temperatures of the fabricated glasses were measured by the temperature gradient technique, and Rietveld refinements were applied to X-Ray powder diffraction (XRD) data for devitrified glasses, enabling quantitative determination of the crystalline and amorphous fractions and the nature of the crystals. Structural properties were investigated by Raman spectroscopy. Acoustic echography, micro-Vicker's indentation, and single-edge-notched bend testing methods were used to measure Young's moduli, hardness, and fracture toughness, respectively. It is shown that it is possible to design lower-melting soda-lime-silica glass compositions without compromising their mechanical and crystallization properties. Unlike Young's modulus, brittleness is highly responsive to the composition in soda-lime-silica glasses, and notably low brittleness values can be obtained in glasses with compositions in the wollastonite primary phase field: an effect that is more pronounced in the silica primary phase field. The measured bulk crystal fractions of the glasses subjected to devitrification at the lowest possible industrial conditioning temperatures indicate that soda-lime-silica glass melts can be conditioned close to their liquidus temperatures within the compositional ranges of the primary phase fields of cristobalite, wollastonite, or their combinations.     

Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals

Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms.     

Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects

This article aims to explore the impact of the integration of risk factors into delayed milestones for construction projects. A simulation model was developed to determine the impact of schedule variability on cost estimation. To generate random scenarios a Monte Carlo Simulation (MCS) technique was applied. The developed model computes the cost impact of delayed milestone in the expected budget. Using a risk integration approach revealed the critical time frame that may lead to a budget deficit for a project. As a result, a number of cost-sensitive risk factors and schedule delays were identified for the critical time period where the risk of budget deficit increases. The method of integration proposed in this article highlights the priority of risk factors and schedule delays for construction contracts involving Payments at Event Occurrences (PEO). Consequently, the developed method can be useful for practitioners in anticipation of potential increase of costs, hence, prevention of failure due to budget deficit.     

A new mixed integer linear programming model for product development using quality function deployment

Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the values of DRs are often discrete instead of continuous. To the best of our knowledge, there is no mixed integer linear programming (MILP) model in which the discrete DRs values are considered. Therefore, in this paper, a new QFD optimization approach combining MILP model and Kano model is suggested to acquire the optimized solution from a limited number of alternative DRs, the values of which can be discrete. The proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete. Additionally, the problem of lack of solutions in integer and linear programming in the QFD optimization is overcome. Finally, the model is illustrated through an example.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.     

Disassembly Sequencing Using Tabu Search

End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included