Hydrodynamic journal bearings have been widely used in heavy industry machinery to support rotating shafts due to their superior durability, low maintenance cost, and excellent load capacity. Inorganic fullerene-like tungsten disulfide nanoparticles (IF-WS2 NPs) are the most studied additive for lubrication purpose due to their excellent mechanical characteristics. In this work, a numerical simulation approach with discrete phase modeling of suspended nanoparticles was used to evaluate the load carrying capacity of journal bearings in heavy industry where the normal loads are very high relative to the bearing footprint. For accurate simulation, nanofluid viscosity was calculated using available empirical models, considering the aggregation effect of NPs. To characterize the aggregations of nanoparticles, scanning electron microscopy (SEM) imaging of the nanofluid samples were employed. The simulated pressure distribution was then used to calculate the load capacity of the bearing. The results show about 20% improvement of load capacity at 5% weight fraction of IF-WS2 nanofluid.

The integration of gold nanoparticles (AuNPs) on the surface of polydimethylsiloxane (PDMS) microfluidics for biosensing applications is a challenging task. In this paper we address this issue by integration of pre-synthesized AuNPs (in a microreactor) into a microfluidic system. This method explored the affinity of AuNPs toward the PDMS surface so that the pre-synthesized particles will be adsorbed onto the channel walls. AuNPs were synthesized inside a microreactor before integration. In order to improve the size uniformity of the synthesized AuNPs and also to provide full mixing of reactants, a 3D-micromixer was designed, fabricated and then integrated with the microreactor in a single platform. SEM and UV/Vis spectroscopy were used to characterize the AuNPs on the PDMS surface.

Gold – poly(dimethylsiloxane) (Au–PDMS) nanocomposites are synthesized in the form of gels, foams, and films. In this chapter, the authors report their results on a novel in situ synthesis of Au–PDMS by using a microwave-induced reduction of gold ions. Microwave heating has been studied as a promising technique for nanoparticle synthesis. Microwave irradiation has been used for the preparation of gold and silver nanoparticles under various conditions. The authors investigate the microwave-induced reduction of gold ions onto a PDMS matrix and the usefulness of the nanocomposite for sensing application. During the interaction between PDMS and the solution, sub-nanometer- sized gold seeds are formed on the surface, which possibly initiate the formation of gold nanoparticles during the irradiation. The gold nanoparticles prepared by microwave irradiation are distributed in the polymer matrix, and to increase the density of nanoparticles on the surface, an annealing process is employed.

Hypothesis/objective: Prolonged QT interval is an index of propensity for dangerous ventricular tachyarrhythmias. The aim of this article is to establish an automatic algorithm for QT interval measurement.Method: The proposed method is based on the continuous wavelet transform. In this method, the concepts of the rescaled wavelet coefficients and dominant scales of the electrocardiogram (ECG) components are used to perform detection of ECG characteristic points. A new concept of rescaled maximum energy density is introduced so as to perform the estimation of the QT interval.Results and conclusion: We have applied the algorithm to the PTB database of the PhysiobanknPhysionet in lead II. Then, the results were evaluated using pertinent reference QT. The criterion used for evaluation of the method's performance is the root mean square (RMS) error. The method approached the RMS error of 27.89 ms for 549 subjects. The proposed method is fast, simple and is applicable to a wide range of ECG cardio cycle morphologies.

Objective: The aim of this study is to found how well one can characterize the location and extent of relatively compact infarcts using electrocardiographic evidence.Method: Here, we address a method base on behavior of some ECG's features, which are Q, amplitudes and ST dispersion. We call these Q and ST curves. At the first step, by plotting the variability of Q, amplitude and ST dispersion for nodes which lies in lines on torso plane, these curves are obtained The behavior of the mentioned curves for normal lines both in horizontal and vertical line differ from the abnormal ones. A threshold method is used here to determine the infarcted area.Results and Conclusion: The method is evaluated on Challenge 2007 database. The results are EPD=8, SO=0.944, and CED=1. The method achieved the best EPD and CED scores and the second place for SO and overall ranked the highest scores (first rank) in CinC/PhysioNet Challenge 2007.

Hypothesis/Objective: The aim of this study is to characterize the location and extent of moderate to large, relatively compact infarcts using ECG evidence. Method: In this paper, we proposed a method on the basis of vectorcardiography which assumes that heart vector is proportional to relevant active depolarization area(s). To examine our ideas, we used the normal VCG which includes the information of location, amplitude, and direction of heart vector at any instant. The model based comparison of cases under study and relevant normal VCGs gives region i.e. segment(s) and depth i.e. extent of myocardial infarction. Results and Conclusion: We evaluated the method on CinC/Physionet Challenge 2007 database. In our final entry the scores of EPD equal to 32 (ranked 3rd), SO equal to 0.933 (ranked 3rd) and CED equal to 1 (ranked 1st) are achieved. It also ranked the third among the other methods proposed to CinC/Physionet Challenge 2007.

An approach to classify disorders in autonomic control of cardiovascular system is proposed in this paper. The target of this study is to highlight main features of malfunctions in cardiovascular system due to autonomic disorder. Collecting the data from the physionet archive, we divide patients into two groups of normal and abnormal, based on having autonomic disorder in their cardiovascular system or not. Systolic blood pressure (SBP) and heart rate (HR) time series are evaluated for each patient. We then plot the diagram of SBP against HR for all patients in a single figure. Fuzzy c-means clustering (FCM) method is also applied to cluster data into two groups. A neural network is then implemented to classify and to distinguish the two groups. The network is trained with data of a normal patient and is tested with data of other normal and abnormal patients. Result show that selected features can clearly detect disorders in autonomic system.

In this paper, a semi-automated multiscale algorithm based on rescaled continuous wavelet transform is presented in order to determine QT interval. According to our previous works, the relation between the duration of ECG waves and their wavelet transforms and dominant scales are used to determine QRS complex vicinity and to denoise ECG signal based on detected vicinity of QRS complex. Then, a simple mathematical sinusoid model for T-wave is considered to determine T-wave domain which is based on variance deviation of T-model and ECG between two successive QRS complexes. A simple analysis of signal’s energy leads to rescaled Maximum Energy Density (MED) curve which is used to determine onset and offset of QT-interval. We evaluated the algorithm on the PTB database. The proposed approach is achieved about 53.7 ms of RMS error. The preliminary results are sent to PhysioNet/Computers in Cardiology Challenge 2006.