Improved approach to Fowler-Nordheim plot analysis

This article introduces an improved approach to Fowler-Nordheim (FN) plot analysis, based on a new type of intercept correction factor. This factor is more cleanly defined than the factor previously used. General enabling theory is given that applies to any type of FN plot of data that can be fitted using a FN-type equation. Practical use is limited to emission situations where slope correction factors can be reliably predicted. By making a series of well-defined assumptions and approximations, it is shown how the general formulas reduce to provide an improved theory of orthodox FN-plot data analysis. This applies to situations where the circuit current is fully controlled by the emitter characteristics, and tunneling can be treated as taking place through a Schottky-Nordheim (SN) barrier. For orthodox emission, good working formulas make numerical evaluation of the slope correction factor and the new intercept correction factor quick and straightforward. A numerical illustration, using simulated emission data, shows how to use this improved approach to derive values for parameters in the full FN-type equation for the SN barrier. Good self-consistency is demonstrated. The general enabling formulas also pave the way for research aimed at developing analogous data-analysis procedures for non-orthodox emission situations.Comment: Paper is extended version of poster presented at the 25th International Vacuum Nanoelectronics Conference, Jeju island, South Korea, July 2012. Third version includes small changes made at proof correction stag

Pharmacogenomics in cardiovascular disorders: Steps in approaching personalized medicine in cardiovascular medicine

Christopher Barone, Shaymaa S Mousa, Shaker A MousaThe Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences, Albany, NY, USAAbstract: Some of the most commonly prescribed medications are those for cardiovascular maladies. The beneficial effects of these medications have been well documented. However, there can be substantial variation in response to these medications among patients, which may be due to genetic variation. For this reason pharmacogenomic studies are emerging across all aspects of cardiovascular medicine. The goal of pharmacogenomics is to tailor treatment to an individual’s genetic makeup in order to improve the benefit-to-risk ratio. This review examines the potential pharmacogenomic parameters which may lead to a future of personalized medicine. For example, it has been found that patients with CYP2C9 and VKORC1 gene variations have a different response to warfarin. Other studies looking at β-blockers, ACE inhibitors, ARBs, diuretics and statins have shown some results linking genetic variations to pharmacologic response. However these studies have not impacted clinical use yet, unlike warfarin findings, as the small retrospective studies need to be followed up by larger prospective studies for definitive results.Keywords: cardiovascular, pharmacogenomics, genetics, cardiovascular medicine, personalized medicine, polymorphis

Autoimmune Lymphoproliferative Syndrome (ALPS): A Case Report

Background: Autoimmune lymphoproliferative syndrome (ALPS) is a rare disorder of the blood, estimated at around 500 cases worldwide. It is characterized by a dysregulation of T-cells in the immune system, and is caused by a defect in the process that mediates leukocyte apoptosis. This may result in an increased risk of lymphoma and autoimmune diseases. Case: The author reports a case of an 11-year-old male who had been followed up since three years of age for recurrent cytopenias, occurring with intermittent breakouts of purpuric rash, nosebleeds, and prolonged infections. Conclusion: A probable diagnosis was made through criteria based on the First International ALPS workshop of 2009. This includes the presence of circulating double-negative T cells, considered the laboratory marker unique for ALPS. The mainstay of treatment was prednisone, given at doses varying in proportion to the severity of immunocytopenia. osis

Polycystic ovary syndrome and its impact on women’s quality of life: More than just an endocrine disorder

In the past, polycystic ovary syndrome has been looked at primarily as an endocrine disorder. Studies now show that polycystic ovary syndrome is a metabolic, hormonal, and psychosocial disorder that impacts a patient’s quality of life. It is extremely important to holistically treat these patients early on to help them deal with the emotional stress that is often overlooked with polycystic ovary syndrome. Early diagnosis and long term management can help control polycystic ovary syndrome so that women can still live a healthy active life and avoid long-term complications such as metabolic syndrome and cardiovascular diseases

Illustrating field emission theory by using Lauritsen plots of transmission probability and barrier strength

This technical note relates to the theory of cold field electron emission (CFE). It starts by suggesting that, to emphasize common properties in relation to CFE theory, the term 'Lauritsen plot' could be used to describe all graphical plots made with the reciprocal of barrier field (or the reciprocal of a quantity proportional to barrier field) on the horizontal axis. It then argues that Lauritsen plots related to barrier strength (G) and transmission probability (D) could play a useful role in discussion of CFE theory. Such plots would supplement conventional Fowler-Nordheim (FN) plots. All these plots would be regarded as particular types of Lauritsen plot. The Lauritsen plots of -G and lnD can be used to illustrate how basic aspects of FN tunnelling theory are influenced by the mathematical form of the tunnelling barrier. These, in turn, influence local emission current density and emission current. Illustrative applications used in this note relate to the well-known exact triangular and Schottky-Nordheim barriers, and to the Coulomb barrier (i.e., the electrostatic component of the electron potential energy barrier outside a model spherical emitter). For the Coulomb barrier, a good analytical series approximation has been found for the barrier-form correction factor; this can be used to predict the existence (and to some extent the properties) of related curvature in FN plots.Comment: Based on a poster presented at the 25th International Vacuum Nanoelectronics Conference, Jeju, S. Korea, July 2012. Version 3 incorporates small changes made at proof stag

Using Artificial Neural Networks to Predict Formation Stresses for Marcellus Shale with Data from Drilling Operations

Artificial neural networks have been applied to different petroleum engineering disciplines. This is contributed to the powerful prediction capability in complex relationships with enough data available. The objective of this study is to develop a new methodology to predict the vertical and horizontal stresses using artificial neural networks for Marcellus shale well laterally drilled in Monongalia County, WV.;This approach coupled the drilling surface measurements with the recorded well logging data. Drilling parameters included depth, WOB, RPM, standpipe pressure, torque, pump flow rate and rate of penetration. Well logging data included gamma ray and bulk density. The model output was the minimum horizontal stress and vertical stress. The well trajectory was divided into two main parts, the vertical and lateral section since the change in the drilling direction along with changing structural geology and sedimentation impacted the resultant stresses.;Several neural networks were designed with a different number of feedforward backpropagation architectures. The collected data was filtered and normalized before neural networks were trained using part of data. A percentage of the data was used to validate the trained model. Finally, a blind data set aside was used to test the model prediction accuracy and to estimate error percentages. Preliminary results show that adding logging data such as gamma ray and bulk density improves the model accuracy. Also, increasing the number of hidden layers and neurons improved the efficiency. However, higher the number of neurons and hidden layers higher was the computational cost due to increased model convergence time.;The correlation coefficients of the predicted and observed values ranged between 0.76 and 0.99. This approach is beneficial regarding hydraulic fracturing design and fracture orientation prediction in unconventional shales

I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

MAXIMUM POWER TRACKING OF A GRID-CONNECTED WIND-DRIVEN BRUSHLESS DOUBLY-FED RELUCTANCE GENERATOR USING SCALAR CONTROL

This paper presents a scalar volt per hertz (v/f) control technique for maximum power tracking of a grid-connected wind-driven Brushless Doubly-Fed Reluctance Generator (BDFRG). The proposed generator has two stator windings namely; power winding, directly connected to the grid, and control winding, connected to the grid through a bi-directional converter. The presented control technique is based on the abc-axis and dq-axis dynamic model of BDFRG. A detailed abc-axis and dq-axis dynamic model, by which the dynamic behaviour of the BDFRG can be successfully predicted under different operating conditions, is presented. In addition, a soft starting method is suggested to avoid the over-current of the bi-directional converter. The presented simulation results ensure the effectiveness of the proposed control strategy for maximum wind-power extraction under wind-speed variations