Easy-plane to easy-axis anisotropy switching in a Co(ii) single-ion magnet triggered by the diamagnetic lattice

Single ion magnets SIMs with large magnetic anisotropy are promising candidates for realization of single molecule based magnetic memory and qubits. Creation of materials with magnetically uncoupled spatially separated SIMs requires dilution in a diamagnetic matrix. Herein, we report that progressive dilution of paramagnetic Co II by diamagnetic Zn II in the SIM [CoxZn 1 amp; 8722;x piv 2 2 NH2 Py 2], x 1 0 beyond a threshold of 50 reveals an abrupt structural change, where the distorted tetrahedral Zn coordination structure is superimposed on the remaining Co ions, which were initially in a distorted octahedral environment. Dilution induced structure modification switches the magnetic anisotropy from easy plane D 36.7 cm amp; 8722;1 to easy axis type D amp; 8722;23.9 cm amp; 8722;1 , accompanied by a fivefold increase of the magnetic relaxation time at 2 K. Changes of the static and dynamic magnetic properties are monitored by electron paramagnetic resonance spectroscopy and AC susceptibility measurements. Complementary quantum chemical ab initio calculations quantify the influence of structural changes on the electronic structure and the magnetic anisotropy. Thus, magnetic dilution hits two goals at once, the creation of isolated magnetic centres and an improvement of their SIM propertie

A large-scale and PCR-referenced vocal audio dataset for COVID-19

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.Comment: 37 pages, 4 figure

Assessing the performance of machine learning methods trained on public health observational data: a case study from COVID-19

From early in the coronavirus disease 2019 (COVID-19) pandemic, there was interest in using machine learning methods to predict COVID-19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing-RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS-CoV-2 infection status and extensive study participant meta-data. This allowed us to rigorously assess state-of-the-art machine learning techniques to predict SARS-CoV-2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks

A large-scale and PCR-referenced vocal audio dataset for COVID-19

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the ‘Speak up and help beat coronavirus’ digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results

Improved microwave method for measuring the dynamic parameters of gasification of condensed materials

A new microwave technique for measuring the transient gasification rate is described. A feature of the technique is that the mass loss during gasification is determined based on measurements of the time-varying resonant frequency of the microwave resonator with the test sample by sequentially recording the resonant characteristics of the sensor. This ensures independence of the measurement results from the change in the Q-value of the resonator during gasification of the propellant sample. A sensor prototype which is a coaxial resonator in which the sample under study is placed in the region of maximum electric field has been tested. Experiments have shown that the sensitivity (the ratio of the change in resonant frequency to the change in the inner diameter of the sample) of the new sensor design is two to four times higher than that of the previous sensor model