Abstract-This paper reports the permanent frequency mismatch reduction of the primary wineglass modes in a planar axisymmetric resonator by strategic mass loading. The resonator consists of a set of concentric rings that are affixed to neighboring rings by a staggered system of spokes. The outer layers of spokes are targets for mass deposition. The paper develops modified ring equations that guide the mass perturbation process and despite the fact that the deposited mass and deposition locations are quantized, it is possible to systematically reduce the frequency difference of the wineglass modes to effective degeneracy such that two modes cannot be distinguished in a frequency response plot. Results on five resonators are reported with nominal wineglass modes near 14 kHz, quality factors of 50 k, and frequency mismatches exceeding 30 Hz in some cases but with post-perturbation mismatches smaller than 80 mHz. Furthermore, it is also shown that the quality factors remain unchanged.

This article includes two studies of reported parent-child relations and sexual identity : one of a population of 84 white, well-educated female homosexuals and their 94 matched heterosexual controls and the other of a group of 127 similarly welleducated, white male homosexuals and their 123 heterosexual matched controls. Female homosexuals reported having had more negative relations with their fathers in childhood that female heterosexuals, although a wide variety of parent-daughter relations was reported by both groups. The female homosexuals were neither mother nor father identified, but they were more distant from both parents and other people than their controls. The female homosexuals also reported a more masculine childhood than the heterosexuals, and they were more masculine on an objective measure of masculinity-femininity. Compared with their controls, the male homosexuals reported more close-binding, intimate mothers and hostile, detached fathers than the heterosexual controls. As with the two female groups, a wide variety of parentson relations was reported. Homosexual males were not more mother identified than their controls, but, like the female group, they were more distant from parents and other people than the matched controls. Male homosexuals reported more feminine childhoods, and they were less masculine than controls on a masculinity-femininity test.

No abstract

Electrostatic tuning of the resonant modes in vibratory gyroscopes is often suggested as a means for compensating manufacturing aberrations that produce detuned resonances. In high performance sensors, however, this approach places very stringent requirements on the stability of the bias voltages used for tuning. Furthermore, the bias voltage stability must be maintained over the operating environment, especially with regard to temperature variations. This paper presents two methods for tuning the resonant modes in MEM vibratory gyroscopes using mass perturbation of the sensor's resonant structure. The approach ameliorates the stringent bias voltage stability requirements and can be applied to any vibratory gyroscope that relies on modal frequency matching for optimum performance.

Ovarian cancer has the sixth-largest fatality rate in the United States among all cancers. A non-surgical assay capable of detecting ovarian cancer with acceptable sensitivity and specificity has yet to be developed. However, such a discovery would profoundly impact the pace of the treatment and improvement to patients’ quality of life. Achieving such a solution requires high-quality imaging, image processing, and machine learning to support an acceptably robust automated diagnosis. In this work, we propose an automated framework that learns to identify ovarian cancer in transgenic mice from optical coherence tomography (OCT) recordings. Classification is accomplished using a neural network that perceives spatially ordered sequences of tomograms. We present three neural network-based approaches, namely a VGG-supported feed-forward network, a 3D convolutional neural network, and a convolutional LSTM (Long Short-Term Memory) network. Our experimental results show that our models achieve a favorable performance with no manual tuning or feature crafting, despite the challenging noise inherent in OCT images. Specifically, our best performing model, the convolutional LSTM-based neural network, achieves a mean AUC (± standard error) of 0.81 ± 0.037. To the best of the authors’ knowledge, no application of machine learning to analyze depth-resolved OCT images of whole ovaries has been documented in the literature. A significant broader impact of this research is the potential transferability of the proposed diagnostic system from transgenic mice to human organs, which would enable medical intervention from early detection of an extremely deadly affliction.