Anosmia and Ageusia as the Only Indicators of Coronavirus Disease 2019 (COVID-19)

The patient is a 60-year-old woman with a history of vertigo and seasonal allergies who presented to the hospital with the chief complaint of headache. Radiological findings were negative for intracranial abnormalities. The headache was due to trigeminal neuralgia. She had concurrent complaints of anosmia and ageusia without fever, respiratory symptoms, or obvious risk factors. However, it was determined to test the patient for coronavirus disease 2019 (COVID-19) infection despite extremely low clinical suspicion. Unfortunately, she was found to be COVID-19 positive after she was discharged from the hospital while she remained asymptomatic. There is currently a lack of published case reports describing COVID-19 patients with the sole symptoms of anosmia and ageusia in the United States of America

Dynamics of a Threshold Shock Sensor: Combining Bi-stability and Triboelectricity

A proof of concept of a triboelectric threshold shock sensor and its characterization are presented. Shock sensors are used in many applications in the automotive, shipping and other industries, mainly to determine if acceleration thresholds are met. Many shock sensors are only mechanical, so the only way to know if the threshold has been reached is to physically check the device. There are noticeable advantages of using triboelectric transduction and bi-stability to create a shock sensor. By combining a buckled-beam structure and a triboelectric generator, we created a proof of concept of a tunable threshold shock sensor. The sensor generates a voltage peak only if the base acceleration is beyond a threshold. In addition, the sensor produces voltage proportional to the base acceleration beyond the threshold acceleration. This means the output signal provides more information about the strength of the shock that the device experiences. The sensor concept is illustrated for a threshold shock of 3.26g, but the threshold can be tuned by increasing the compressive axial force of the buckled beam. Increasing this axial force increases the threshold shock the sensor can detect. Thus, the combined system is a tunable threshold shock sensor with enhanced functionality. We presented a mathematical model that captures important observations of the experiments and can be used as a design tool for more precise, high-resolution triboelectric shock sensors

A tunable triboelectric wideband energy harvester

The ability to efficiently convert mechanical energy into electrical energy has become an important topic of discussion and research in the last decade. Triboelectric generators have recently been popular for vibration energy harvesting, but despite plenty of research on its material aspect, research on combining mechanical characteristics and voltage generation output has been sparse. Many energy harvesters suffer from low operating bandwidths and are usually restricted to operating at a specific frequency. We propose a tunable triboelectric energy harvester that has a large response over a wide frequency bandwidth at low frequencies. The tunability is implemented by axially pre-loading a beam that reduces the system stiffness. This stiffness reduction strengthens the collisions that naturally occur in the triboelectric generators, resulting in larger voltage outputs. As the system stiffness decreases, the impacts occur over a broader frequency range, widening the frequency bandwidth. To describe the dynamic and voltage responses, a continuous electromechanical model is derived. The presented mathematical model sheds light on the coupled characteristics of mechanical vibration and triboelectric voltage generation, and can be used as a design tool for high-efficiency energy harvesters to operate wireless sensor networks

Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow

Recent studies incorporate Nesterov's accelerated gradient method for the acceleration of gradient based training. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to drastically improve the convergence speed compared to the conventional quasi-Newton method. This paper implements NAQ for non-convex optimization on Tensorflow. Two modifications have been proposed to the original NAQ algorithm to ensure global convergence and eliminate linesearch. The performance of the proposed algorithm - mNAQ is evaluated on standard non-convex function approximation benchmark problems and microwave circuit modelling problems. The results show that the improved algorithm converges better and faster compared to first order optimizers such as AdaGrad, RMSProp, Adam, and the second order methods such as the quasi-Newton method.Comment: Paper published in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA

A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method

Recently algorithms incorporating second order curvature information have become popular in training neural networks. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to effectively accelerate the BFGS quasi-Newton method by incorporating the momentum term and Nesterov's accelerated gradient vector. A stochastic version of NAQ method was proposed for training of large-scale problems. However, this method incurs high stochastic variance noise. This paper proposes a stochastic variance reduced Nesterov's Accelerated Quasi-Newton method in full (SVR-NAQ) and limited (SVRLNAQ) memory forms. The performance of the proposed method is evaluated in Tensorflow on four benchmark problems - two regression and two classification problems respectively. The results show improved performance compared to conventional methods.Comment: Accepted in ICMLA 201

Associations Between Components of Metabolic Syndrome and Cognition in Patients With Schizophrenia

The metabolic syndrome and cognitive dysfunctions are common in patients with schizophrenia, yet there is no general consensus concerning the effects of the components of the metabolic syndrome on various cognitive domains. The goal of this study was to investigate the relationship between components of the metabolic syndrome and cognition in patients with schizophrenia. Components of the metabolic syndrome and neurocognitive functioning were assessed in 68 patients with schizophrenia. The Brief Assessment of Cognition in Schizophrenia (BACS) was used to assess neurocognition. Hyperglycemia and hypertension were the only components of the metabolic syndrome found to be associated with cognitive functioning. Patients with schizophrenia who were hypertensive showed cognitive impairments in 2 domains, with a negative association found between hypertension and verbal memory (P=0.047) and verbal fluency (P=0.007). Hyperglycemia was associated with higher scores on verbal memory (P=0.01) and verbal fluency (P<0.001). It appears that medical treatment of certain components of the metabolic syndrome could affect cognitive performance in patients with schizophrenia