Systematic investigation of mechanical properties and fracture toughness of epoxy networks: Role of the polyetheramine structural parameters

Polyetheramine (PEA)-modified epoxies with various types of PEAs were prepared and respective effects on characteristics of epoxy networks were studied. The used PEAs were polyethylene glycol diamine (PEG-amine) and polypropylene glycol diamine (PPG-amine) with two different molecular weights (i.e., 200 and 400 g mol⁻¹). According to mechanical tests, the structural parameters of PEAs played an important role in final properties of epoxy/amine systems. PEG₄₀₀-amine and PPG₂₀₀-amine had the highest and lowest effects on the properties of epoxy networks, respectively. Whereas 10 phr PEG₄₀₀-amine increased critical stress intensity factor (K_IC) and critical strain energy release rate (G_IC) of the epoxy up to 82 and 294%, the same number of PPG₂₀₀-amine chains caused to increase the K_IC and G_IC up to 11 and 34%. This discrepancy could be assigned to higher flexibility index (φ = 26.22), longer chain length (~27 atoms), and higher secondary interactions [δ = 9.69 (cal cm⁻³)^0.5] of PEG₄₀₀-amine in comparison with PPG₂₀₀-amine [with φ = 8.08, ~10 atoms in chain, and δ = 8.98 (cal cm⁻³)^0.5]. Shear yielding as a toughening mechanism was proposed based on microscopy of the crack tips. These in-depth studies could uncover underlying structure–property relationships in a relevant class of PEA-like modifiers, shedding light on the future design of top-performing homogeneous tough polymer networks. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47121.     

Effect of Stearic Acid as a Co‐solvent on the Solubility Enhancement of Aspirin in Supercritical CO2

The solubility of aspirin in supercritical CO₂ (SC‐CO₂) with stearic acid as a co‐solvent was measured at various pressures and temperatures. The experimental data were obtained by a static method. Stearic acid had a significant effect on the enhancement of solubility, as the aspirin solubility increased by up to 16 times. Further, the effect of stearic acid on the solubility enhancement of aspirin was compared with that of other co‐solvents. Different semi‐empirical models from the literature were applied for correlating the experimental data, proving good agreement with the experimental data. The model of Sung and Shim exhibited the lowest deviation from the obtained data. The results of the solubility test can be employed to produce aspirin‐based pharmaceuticals using supercritical fluid technology (SFT).     

Symmetric and non-symmetric Lambertson magnets

A set of symmetric and non-symmetric Lambertson magnets has been designed for the injection, extraction, and abort line of Fermi National Accelerator Laboratory superconducting synchrotron. Techniques, measurements, and detailed analysis of both body and end field are presented.     

An online learning fuzzy controller

A fuzzy controller with online learning capability is reported in this paper. The controller learns from a standard proportional plus derivative (PD) controller. It is implicitly assumed that the tuning parameters of the PD controller are already known. The learning is realized via Wang's table lookup scheme. The controllers are applied successfully to control an open-loop unstable system, i.e., the ball and plate system. Experimental studies have demonstrated the performance of the proposed controller.     

Adaptive fuzzy approach for a class of uncertain nonlinear systems in strict-feedback form

Adaptive fuzzy control is proposed for a class of affine nonlinear systems in strict-feedback form with unknown nonlinearities. The unknown nonlinearities include two types of nonlinear functions: one satisfies the "triangularity condition" and can be directly approximated by fuzzy logic system, while the other is assumed to be partially known and consists of parametric uncertainties. Takagi-Sugeno type fuzzy approximators are used to approximate unknown system nonlinearities and the design procedure is a combination of adaptive backstepping and generalized small gain design techniques. It is proved that the proposed adaptive control scheme can guarantee the uniformly ultimately bounded (UBB) stability of the closed-loop systems. Simulation studies are shown to illustrate the effectiveness of the proposed approach.     

A multi-view-group non-negative matrix factorization approach for automatic image annotation

Multimedia Tools and Applications - In automatic image annotation (AIA) different features describe images from different aspects or views. Part of information embedded in some views is common for...     

Visual requirements analytics: a framework and case study

For many software projects, keeping requirements on track needs an effective and efficient path from data to decision. Visual analytics creates such a path that enables the human to extract insights by interacting with the relevant information. While various requirements visualization techniques exist, few have produced end-to-end value to practitioners. In this paper, we advance the literature on visual requirements analytics by characterizing its key components and relationships in a framework. We follow the goal–question–metric paradigm to define the framework by teasing out five conceptual goals (user, data, model, visualization, and knowledge), their specific operationalizations, and their interconnections. The framework allows us to not only assess existing approaches, but also create tool enhancements in a principled manner. We evaluate our enhanced tool support through a case study where massive, heterogeneous, and dynamic requirements are processed, visualized, and analyzed. Working together with practitioners on a contemporary software project within its real-life context leads to the main finding that visual analytics can help tackle both open-ended visual exploration tasks and well-structured visual exploitation tasks in requirements engineering. In addition, the study helps the practitioners to reach actionable decisions in a wide range of areas relating to their project, ranging from theme and outlier identification, over requirements tracing, to risk assessment. Overall, our work illuminates how the data-to-decision analytical capabilities could be improved by the increased interactivity of requirements visualization.     

Performance Assessment of the Linear,Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting

In recent years, the data-driven modeling techniques have gained more attention in hydrology and water resources studies. River runoff estimation and forecasting are one of the research fields that these techniques have several applications in them. In the current study, four common data-driven modeling techniques including multiple linear regression, K-nearest neighbors, artificial neural networks and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using of some different scenarios for selecting predictor variables have been studied. It is evident from the results that using flow data of one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, the selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.     

Morphological region-based initial contour algorithm for level set methods in image segmentation

Multimedia Tools and Applications - Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to...