Predicting thermal degradation kinetics of nylon6/feather keratin blends using artificial intelligence techniques

A multi-structured architecture of artificial intelligence techniques including artificial neural network (ANN), adaptive-neuro-fuzzy-interference system (ANFIS) and radial basis function (RBF) were developed to predict thermal degradation kinetics (TDK) of nylon6 (NY6)/feather keratin (FK) blend films. By simultaneous implementation of back-propagation ANN and feed-forward ANFIS modeling on the experimental data obtained from thermogravimetric analysis (TGA) method, thermal degradation behavior of various compositions of NY6/FK blends was successfully predicted with minimum mean square errors (MSE). RBF networks were then trained on the TGA data at one heating rate for predicting analogs information at different heating rates, providing sufficient feed for TDK modeling. According to the comparison made between experimental and predicted kinetic parameters of thermal degradation process calculated from Friedman and Kissinger methods, the proposed prediction effort could effectively contribute to the estimation of precise activation energy (E_a) and reaction order (n) values with least amount of experimental work and most accuracy.     

Enhanced response inhibition during intensive meditation training predicts improvements in self-reported adaptive socioemotional functioning.

We examined the impact of training-induced improvements in self-regulation, operationalized in terms of response inhibition, on longitudinal changes in self-reported adaptive socioemotional functioning. Data were collected from participants undergoing 3 months of intensive meditation training in an isolated retreat setting (Retreat 1) and a wait-list control group that later underwent identical training (Retreat 2). A 32-min response inhibition task (RIT) was designed to assess sustained self-regulatory control. Adaptive functioning (AF) was operationalized as a single latent factor underlying self-report measures of anxious and avoidant attachment, mindfulness, ego resilience, empathy, the five major personality traits (extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience), difficulties in emotion regulation, depression, anxiety, and psychological well-being. Participants in Retreat 1 improved in RIT performance and AF over time whereas the controls did not. The control participants later also improved on both dimensions during their own retreat (Retreat 2). These improved levels of RIT performance and AF were sustained in follow-up assessments conducted approximately 5 months after the training. Longitudinal dynamic models with combined data from both retreats showed that improvement in RIT performance during training influenced the change in AF over time, which is consistent with a key claim in the Buddhist literature that enhanced capacity for self-regulation is an important precursor of changes in emotional well-being. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Effect of Complex Ceramic Oxide Nanoparticles Addition on the Corrosion Behavior of Stainless Steel in Artificial Sea Water: Physical Chemistry Approach

Protection of Metals and Physical Chemistry of Surfaces - A novel method of electrochemical synthesis of yttrium aluminum garnet (YAG: Al5Y3O12) is successfully developed in the mixture of YCl3 and...     

Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns

This study develops three neural networks models for estimating daily pan evaporation (PE) in South Korea: multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and adaptive neuro-fuzzy inference system (ANFIS). Daily PE was estimated at Daegu and Ulsan stations using temperature-based, radiation-based, sunshine duration-based and merged input combinations under lag-time patterns. Daily evaporation values computed by the models using merged inputs agreed with observed values. Comparison was also made between the neural networks models and multiple linear regression model (MLRM), which showed the superiority of MLP-NNM, GRNNM, and ANFIS over MLRM. It is concluded that the applied neural networks models can be successfully employed for estimating daily PE in South Korea.     

Entanglement of three-level atomic system and spontaneous emission fields in a vacuum and near the 1D photonic crystal band gap

The time evolution of the quantum entropy in a GaAs/AlAs one-dimensional photonic crystal (1DPC) with an atomic system defect layer is investigated in this work. The entanglement between atomic system and their spontaneous emission fields near the edge of the photonic band gap (PBG) is coherently controlled by the coupling field. Comparison between the atom-photon entanglement of the atomic system in the vacuum surrounding and that near the PBG of the 1DPC shows that the degree of entanglement strongly depends on the PBG. We find that degree of entanglement is strongly dependent on the intensity and detuning of the coupling and probe fields. Furthermore, the effect of the phase difference between applied fields on the atom-photon entanglement is studied. The potentially possible technological applications can be provided by the proposed model in the quantum optics and quantum communications based on photonic crystal.     

Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations

This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting. Five different GP and ANFIS models comprising various combinations of water table depth values from two stations, Bondville and Perry, are developed to forecast one-, two- and three-day ahead water table depths. The root mean square errors (RMSE), scatter index (SI), Variance account for (VAF) and coefficient of determination (R²) statistics are used for evaluating the accuracy of models. Based on the comparisons, it was found that the GP and ANFIS models could be employed successfully in forecasting water table depth fluctuations. However, GP is superior to ANFIS in giving explicit expressions for the problem.     

Creeping dispositionism: The temporal dynamics of behavior prediction.

Four studies tested the hypothesis that temporal distance increases the weight of global dispositions in predicting and explaining future behavior. Study 1 found that the correspondence bias was manifested more strongly in predictions of distant future behavior than of near future behavior. Study 2 found that participants predicted higher cross-situational consistency in distant future behavior than in near future behavior. Study 3 found that participants sought information about others' more global dispositions for predicting distant future than near future behavior. Finally, Study 4 found that participants made more global causal attributions for distant future outcomes than for near future outcomes. The results were interpreted as supporting the assumption of construal level theory that perceivers use more abstract representations (higher level construals) to predict and explain more distant future behaviors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Predicting the near and distant future.

Four studies investigated individuals' confidence in predicting near future and distant future outcomes. Study 1 found that participants were more confident in theory-based predictions of psychological experiments when these experiments were expected to take place in the more distant future. Studies 2-4 examined participants' confidence in predicting their performance on near and distant future tests. These studies found that in predicting their more distant future performance, participants disregarded the format of the questions (e.g., multiple choice vs. open ended) and relied, instead, on their perceived general knowledge (e.g., history knowledge). Together, the present studies demonstrate that predictions of the more distant future are based on relatively abstract information. Individuals feel more confident in predicting the distant future than the near future when the predictions concern outcomes that are implied by relatively abstract information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)