Predicting the spontaneous termination of atrial fibrillation based on poincare section in the electrocardiogram phase space

Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets. The results showed that 86.67% of dataset A and 80% of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination.     

Application of artificial neural network for vapor liquid equilibrium calculation of ternary system including ionic liquid: Water, ethanol and 1-butyl-3-methylimidazolium acetate

A feed forward three-layer artificial neural network (ANN) model was developed for VLE prediction of ternary systems including ionic liquid (IL) (water+ethanol+1-butyl-3- methyl-imidazolium acetate), in a relatively wide range of IL mass fractions up to 0.8, with the mole fractions of ethanol on IL-free basis fixed separately at 0.1, 0.2, 0.4, 0.6, 0.8, and 0.98. The output results of the ANN were the mole fraction of ethanol in vapor phase and the equilibrium temperature. The validity of the model was evaluated through a test data set, which were not employed in the training case of the network. The performance of the ANN model for estimating the mole fraction and temperature in the ternary system including IL was compared with the non-random-two-liquid (NRTL) and electrolyte non-random-two-liquid (eNRTL) models. The results of this comparison show that the ANN model has a superior performance in predicting the VLE of ternary systems including ionic liquid.     

A crossover‐UNIQUAC model for critical and noncritical LLE calculations

A new model, named the crossover‐UNIQUAC model, has been proposed based on the crossover procedure for predicting constant‐pressure liquid–liquid equilibria (LLE). In this manner, critical fluctuations were incorporated into the classical UNIQUAC equation. Coexistence curves were estimated for systems having a diverse range of asymmetries. These systems included the LLE of five different mixtures, composed of nitrobenzene with one of the members of the alkane homologous family (either pentane, octane, decane, dodecane, or tetradecane), as well as an extra system having a different chemical nature, namely the mixture of n‐perfluorohexane and hexane, to further check the validity of the proposed approach. Using these nonideal mixtures, the validity of the new model was investigated within wide ranges, covering near‐critical to regions falling far away from the critical point. The graphical trends, as well as the quantitative comparison with experimental data indicated the good agreement of the proposed model results with the experimental data. A maximum AARD% value of 3.97% was obtained in calculating molar compositions by the proposed model for such challenging systems covering noncritical, as well as critical regions. In addition, to show the strength of the proposed crossover approach to describe properties other than LLE, molar heat capacities were investigated for the system of nitrobenzene + dodecane. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3094–3103, 2015     

Engaging in activities involving information technology: dimensions, modes, and flow

An engagement mode involves a subject (e.g., a user of information technology, or IT) who is engaged in an activity with an object in a certain manner (the mode). The purpose of this study is to develop a general model of engagement modes that may be used for understanding how IT-related activities are shaped by properties of the user and the IT object. A questionnaire involving items on IT engagement and the experience of flow was administered to 300 participants. The results supported an engagement mode (EM) model involving 5 different engagement modes (enjoying/acceptance, ambition/curiosity, avoidance/hesitation, frustration/ anxiety, and efficiency/productivity) characterized on 3 dimensions (evaluation of object, locus of control between subject and object, and intrinsic or extrinsic focus of motivation). The flow experience follows from a balance between enjoying/ acceptance and efficiency/productivity propelled by ambition/curiosity. The EM model could provide a platform for considering how IT users, IT applications, and IT environments should work together to yield both enjoyment and efficiency. Actual or potential applications of this research include designing IT training programs on different levels of specificity.     

Reflection and transmission coefficient of yttrium iron garnet filled polyvinylidene fluoride composite using rectangular waveguide at microwave frequencies

The sol-gel method was carried out to synthesize nanosized Yttrium Iron Garnet (YIG). The nanomaterials with ferrite structure were heat-treated at different temperatures from 500 to 1000 °C. The phase identification, morphology and functional groups of the prepared samples were characterized by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), respectively. The YIG ferrite nanopowder was composited with polyvinylidene fluoride (PVDF) by a solution casting method. The magnitudes of reflection and transmission coefficients of PVDF/YIG containing 6, 10 and 13% YIG, respectively, were measured using rectangular waveguide in conjunction with a microwave vector network analyzer (VNA) in X-band frequencies. The results indicate that the presence of YIG in polymer composites causes an increase in reflection coefficient and decrease in transmission coefficient of the polymer.     

Robust weighted graph transformation matching for rigid and nonrigid image registration

This paper presents an automatic point matching algorithm for establishing accurate match correspondences in two or more images. The proposed algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. The algorithm starts with a set of matches (including outliers) between the two images. A set of nondirectional graphs is then generated for each feature and its K nearest matches (chosen from the initial set). Using the angular distances between edges that connect a feature point to its K nearest neighbors in the graph, the algorithm finds a graph in the second image that is similar to the first graph. In the case of a graph including outliers, the algorithm removes such outliers (one by one, according to their strength) from the graph and re-evaluates the angles until the two graphs are matched or discarded. This is a simple intuitive and robust algorithm that is inspired by a previous work. Experimental results demonstrate the superior performance of this algorithm under various conditions, such as rigid and nonrigid transformations, ambiguity due to partial occlusions or match correspondence multiplicity, scale, and larger view variation.     

Effects of washing pre-treatment on mercury concentration in fish tissue

The objective of this study was to examine the effect of washing pre-treatment on mercury concentration in fish fillet. Response surface methodology was used to investigate the influence of three variables, pH (1–6.5), NaCl (0–1% w/v) and exposure time (5–30 min) by using a three-factor central composite design. The aim was to obtain the best possible combination of these variables in order to reduce mercury in fish fillet. The experimental data were adequately fitted into a second-order polynomial model with multiple regression coefficients (R ²) of 0.961. The results indicated that the reduction of mercury in fish flesh significantly depends on the pH of the solution used. The overall optimal condition resulting in the maximum mercury reduction in fish fillet was obtained at a combined level pH of 2.79, NaCl of 0.5% and exposure time of 13.5 min. The optimized protocol produced a solution that can reduce mercury from raw fish fillet up to 81%.     

A Review on Mycotoxins in Food and Feed: Malaysia Case Study

Fungi are distributed worldwide and can be found in various foods and feedstuffs from almost every part of the world. Mycotoxins are secondary metabolites produced by some fungal species and may impose food safety risks to human health. Among all mycotoxins, aflatoxins (AFs), ochratoxin A (OTA), trichothecenes, deoxynivalenol (DON and T‐2 toxin), zearalenone (ZEN), and fumonisins (FMN) have received much attention due to high frequency and severe health effects in humans and animals. Malaysia has heavy rainfall throughout the year, high temperatures (28 to 31 °C), and high relative humidity (70% to 80% during wet seasons). Stored crops under such conditions can easily be contaminated by mycotoxin‐producing fungi. The most important mycotoxins in Malaysian foods are AFs, OTA, DON, ZEN, and FMN that can be found in peanuts, cereal grains, cocoa beans, and spices. AFs have been reported to occur in several cereal grains, feeds, nuts, and nut products consumed in Malaysia. Spices, oilseeds, milk, eggs, and herbal medicines have been reported to be contaminated with AFs (lower than the Malaysian acceptable level of 35 ng/g for total AFs). OTA, a possible human carcinogen, was reported in cereal grains, nuts, and spices in Malaysian market. ZEN was detected in Malaysian rice, oat, barley, maize meal, and wheat at different levels. DON contamination, although at low levels, was reported in rice, maize, barley, oat, wheat, and wheat‐based products in Malaysia. FMN was reported in feed and some cereal grains consumed in Malaysia. Since some food commodities are more susceptible than others to fungal growth and mycotoxin contamination, more stringent prevention and control methods are required.     

A walk around the decarboxylative C-S cross-coupling reactions

This review highlights a number of recent discoveries and advances in the construction of carbon-sulfur bonds through decarboxylative cross-coupling reactions between carboxylic acids and sulfur-containing coupling partners. Particular emphasis is placed on the mechanistic aspects of the reactions.