Determination of compounding and external load characteristics of saturated cylindrical-rotor synchronous generators

The paper describes a method for predicting the load (compounding or excitation and external) characteristics of saturated cylindrical-rotor synchronous machines. The method is based upon the phasor diagram representation of a synchronous machine and utilizes a simple formula for approximating the magnetisation curve. This yields a closed form of expression for the excitation characteristics, while solution of the equations of the external load characteristics is carried out, easily, by using a simple iteration process. The validity of the approach is verified by tests.     

Equations of heat generation during friction stir welding for tapered polygonal tools

ABSTRACT

Owing to the complex nature of heat generation in friction stir welding, equations are required to understand the effect of process parameters and tool geometry on heat generation. In this study, simplified equations for straight tool profiles have been extended to treat tapered tool profiles for triangular, square, pentagonal and hexagonal geometries. New equations have been implemented to model heat generation in a finite element software package for welding aluminium alloy. The calculated thermal profiles agree better with experimental data than those calculated using the simplified equations. It was also demonstrated that the amount of heat generation increases with increasing number of flats on the tapered tool profile, with a hexagonal tapered tool profile generating the highest temperature.     

Computer Graphics and an Interactive Stereotactic System for CT-Aided Neurosurgery

Using digital CT image data transformed to a patient-frame coordinate system, neurosurgeons can simulate, plan, and execute their procedures with submillimeter precision?all in the CT suite.     

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer‐related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)‐based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP‐based prediction models. Prediction performance evaluations and comparisons between the authors’ GEP models and three representative machine learning methods, support vector machine, multi‐layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross‐data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.Inspec keywords: lung, cancer, medical diagnostic computing, patient diagnosis, genetic algorithms, feature selection, learning (artificial intelligence), support vector machines, multilayer perceptrons, radial basis function networks, reliability, sensitivity analysisOther keywords: lung cancer prediction, cancer‐related death, cancer diagnosis, gene profiles, gene expression programming‐based model, gene selection, GEP‐based prediction models, prediction performance evaluations, representative machine learning methods, support vector machine, multilayer perceptron, radial basis function neural network, real microarray lung cancer datasets, cross‐data set validation, reliability, receiver operating characteristic curve     

RANTES chemokine expression is related to acute cardiac cellular rejection and infiltration by CD45RO T-lymphocytes and macrophages

BACKGROUND: Despite the advances made in immunosuppression therapy, episodes of acute cellular rejection may affect graft function and survival. We investigated the role of RANTES in cellular recruitment and in cardiac allograft rejection. METHODS: Endomyocardial biopsies (n = 65) from 30 patients were taken at various times after transplantation. In 4 subjects who died of acute cellular rejection, the profile of RANTES expression was monitored in all biopsy specimens and in postmortem tissue. Myocardial tissue from 10 other transplants was also analyzed. Sections were stained with an anti-human RANTES antibody with the streptavidin-biotin technique. RANTES-positive cells were related to macrophage, CD45RO "memory" T-cell, and eosinophil infiltration. RESULTS: RANTES-positive cells were identified within the cellular infiltrate in 95% of biopsies with moderate/severe rejection and 28% with mild rejection. RANTES-positive, CD45RO-positive, and macrophage cell numbers were higher in subjects who died of acute cellular rejection than of other causes. A highly significant difference in RANTES-positive cell number was observed between moderate/severe, mild, and nonrejection groups (p = .0001) and correlated significantly with macrophage number in both right and left ventricles (r = .693, p < .01; r = .599, p < .05, respectively) and with the number of "memory" T cells (r = .829, p < .001; r = .779, p < .01, respectively). CONCLUSIONS: These findings suggest that local release of RANTES is important in the recruitment of both macrophages and CD45RO T cells in cardiac allograft rejection. RANTES may be an important chemokine to target for therapeutic intervention in heart rejection.     

Cancer adjuvant chemotherapy prediction model for non‐small cell lung cancer

Non‐small cell lung cancer (NSCLC) is the most popular and dangerous type of lung cancer. Adjuvant chemotherapy (ACT) is the main treatment after surgery resection to prevent the patient from cancer recurrence. However, ACT could be toxic and unhelpful in some cases. Therefore, it is highly desired in clinical applications to predict the treatment outcomes of chemotherapy. Conventional methods of predicting cancer treatment rely solely on histopathology and the results are not reliable in some cases. This study aims at building a predictive model to identify who needs ACT treatment and who should avoid it. To this end, the authors propose an innovative method to identify NSCLC‐related prognostic genes from microarray gene‐expression datasets. They also propose a new model using gene‐expression programming algorithm for ACT classification. The proposed model was evaluated on integrated microarray datasets from four institutes and compared with four representative methods: general regression neural network, decision tree, support vector machine and naive Bayes. Evaluation results demonstrated the effectiveness of the proposed model with accuracy 89.8% which is higher than other representative models. They obtained four probes (four genes) that can get good prediction results. These genes are 204891_s_at (LCK), 208893_s_at (DUSP6), 202454_s_at (ERBB3) and 201076_at (MMD).Inspec keywords: neural nets, regression analysis, decision trees, surgery, medical computing, cancer, cellular biophysics, lung, genetics, support vector machines, Bayes methods, biochemistryOther keywords: cancer ACT prediction model, nonsmall cell lung cancer, adjuvant chemotherapy, surgery resection, cancer recurrence, conventional methods, cancer treatment, microarray gene‐expression technology, NSCLC treatment, ACT treatment, NSCLC‐related prognostic genes, microarray gene‐expression datasets, gene‐expression programming algorithm, ACT classification, ACT information, integrated microarray datasets, representative models, survival time, general regression neural network, decision tree, support vector machine, naive Bayes     

Average framing linear prediction coding with wavelet transform for text-independent speaker identification system

In this work, an average framing linear prediction coding (AFLPC) technique for text-independent speaker identification systems is presented. Conventionally, linear prediction coding (LPC) has been applied in speech recognition applications. However, in this study the combination of modified LPC with wavelet transform (WT), termed AFLPC, is proposed for speaker identification. The investigation procedure is based on feature extraction and voice classification. In the phase of feature extraction, the distinguished speaker’s vocal tract characteristics were extracted using the AFLPC technique. The size of a speaker’s feature vector can be optimized in term of an acceptable recognition rate by means of genetic algorithm (GA). Hence, an LPC order of 30 is found to be the best according to the system performance. In the phase of classification, probabilistic neural network (PNN) is applied because of its rapid response and ease in implementation. In the practical investigation, performances of different wavelet transforms in conjunction with AFLPC were compared with one another. In addition, the capability analysis on the proposed system was examined by comparing it with other systems proposed in literature. Consequently, the PNN classifier achieves a better recognition rate (97.36%) with the wavelet packet (WP) and AFLPC termed WPLPCF feature extraction method. It is also suggested to analyze the proposed system in additive white Gaussian noise (AWGN) and real noise environments; 58.56% for 0 dB and 70.52% for 5 dB. The recognition rates for the whole database of the Gaussian mixture model (GMM) reached the lowest value in case of small number of training samples.     

Osseointegration evaluation of laser-deposited titanium dioxide nanoparticles on commercially pure titanium dental implants

The nanotechnology field plays an important role in the improvement of dental implant surfaces. However, the different techniques used to coat these implants with nanostructured materials can differently affect cells, biomolecules and even ions at the nano scale level. The aim of this study is to evaluate and compare the structural, biomechanical and histological characterization of nano titania films produced by either modified laser or dip coating techniques on commercially pure titanium implant fixtures. Grade II commercially pure titanium rectangular samples measuring 35?×?12?×?0.25?mm length, width and thickness, respectively were coated with titania films using a modified laser deposition technique as the experimental group, while the control group was dip-coated with titania film. The crystallinity, surface roughness, histological feature, microstructures and removal torque values were investigated and compared between the groups. Compared with dip coating technique, the modified laser technique provided a higher quality thin coating film, with improved surface roughness values. For in vivo examinations, forty coated screw-designed dental implants were inserted into the tibia of 20 white New Zealand rabbits’ bone. Biomechanical and histological evaluations were performed after 2 and 4 weeks of implantation. The histological findings showed a variation in the bone response around coated implants done with different coating techniques and different healing intervals. Modified laser-coated samples revealed a significant improvement in structure, surface roughness values, bone integration and bond strength at the bone-implant interface than dip-coated samples. Thus, this technique can be an alternative for coating titanium dental implants.