Dataset on broadband electrochemical impedance spectroscopy of Lithium-Ion batteries for different values of the state-of-charge

This dataset consists of electrochemical impedance spectroscopy measurements on commonly-used batteries, namely Samsung ICR18650-26J cylindrical Lithium-Ion cells. The complex impedance of the batteries was measured at a set of fourteen different frequencies from 0.05 Hz to 1000 Hz, using a random-phase multi-sine excitation signal. For each excited frequency, the current amplitude was 50 mA, resulting in a measurement uncertainty of approximately 0.1 mΩ. Six measurement repetitions are provided at ten different states-of-charge of four different brand-new batteries. Repeated EIS measurement results were obtained, for each individual battery cell, from six separate discharge cycles. All measurements were performed with the battery placed in a temperature-controlled chamber at 25 ± 1 °C. Batteries were allowed to thermalize before each measurement

Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study

Objectives Online health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users. Setting and design We obtained data from three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1 January 2020 to 31 May 2020. Using NLP, we analysed the content of posts related to COVID-19. Primary outcome measures (1) Proportion of forum posts containing COVID-19 keywords; (2) proportion of forum users making their very first post about COVID-19; (3) proportion of COVID-19-related posts containing content related to physical and mental health comorbidities. Results Data from 739 434 posts created by 53 134 unique users were analysed. A total of 35 581 posts (4.8%) contained a COVID-19 keyword. Posts discussing COVID-19 and related comorbid disorders spiked in early March to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. Over a quarter of COVID-19-related thread titles mentioned a physical or mental health comorbidity. Conclusions We demonstrate that it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. The pandemic and corresponding public response has had a significant impact on posters’ queries regarding mental health. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance

On the use of skin texture features for gender recognition: An experimental evaluation

© 2016 IEEE.Skin appearance is almost universally the object of gender-related expectations and stereotypes. This not with standing, remarkably little work has been done on establishing quantitatively whether skin texture can be used for gender discrimination. We present a detailed analysis of the skin texture of 43 subjects based on two complementary imaging modalities afforded by a visible-light dermoscope and the recently developed Epsilon sensor for capacitive imaging. We consider an array of established texture features in combination with two supervised classification techniques (1-NN and SVM) and a state-of-the-art unsupervised approach (t-SNE). A statistical analysis of the results suggests that skin microtexture carries very little information on gender

Development and automation of a test of impulse control in zebrafish.

Deficits in impulse control (difficulties in inhibition of a pre-potent response) are fundamental to a number of psychiatric disorders, but the molecular and cellular basis is poorly understood. Zebrafish offer a very useful model for exploring these mechanisms, but there is currently a lack of validated procedures for measuring impulsivity in fish. In mammals, impulsivity can be measured by examining rates of anticipatory responding in the 5-choice serial reaction time task (5-CSRTT), a continuous performance task where the subject is reinforced upon accurate detection of a briefly presented light in one of five distinct spatial locations. This paper describes the development of a fully-integrated automated system for testing impulsivity in adult zebrafish. We outline the development of our image analysis software and its integration with National Instruments drivers and actuators to produce the system. We also describe an initial validation of the system through a one-generation screen of chemically mutagenized zebrafish, where the testing parameters were optimized

Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot