Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients

Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of lymphocytes from days 1 to 15 after admission to hospital) are associated with relapse rates and evaluations of COVID-19 infections. Accordingly, the severity of COVID-19 is classified in two categories, serious and non-serious. The experimental results indicate that the proposed CQNN’s prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.     

Parameter identification of two dimensional digital filters using electro-magnetism optimization

Multimedia Tools and Applications - The design of Two-Dimensional Infinite Input Response Filters (2D IIR) is an important task in the field of signal processing. These filters are widely used in...     

Towards analysing the rationale of information security non-compliance: Devising a Value-Based Compliance analysis method

Employees’ poor compliance with information security policies is a perennial problem. Current information security analysis methods do not allow information security managers to capture the rationalities behind employees’ compliance and non-compliance. To address this shortcoming, this design science research paper suggests: (a) a Value-Based Compliance analysis method and (b) a set of design principles for methods that analyse different rationalities for information security. Our empirical demonstration shows that the method supports a systematic analysis of why employees comply/do not comply with policies. Thus we provide managers with a tool to make them more knowledgeable about employees’ information security behaviours.     

Extraction and quantification of phytoestrogens in foods using automated solid-phase extraction and LC/MS/MS

Phytoestrogens are a group of polyphenolic plant metabolites that can induce biological responses. Their bioactivity is based on their similarity to 17beta-estradiol and their ability to bind to the beta-estrogen receptor. Although epidemiological data are inconclusive, phytoestrogens are considered to be beneficial for a variety of conditions, for example, hormone-related cancers like breast and prostate cancer. To investigate the biological effects of these compounds and to assess the exposure of larger cohorts or the general public, reliable data on the phytoestrogen content of food is necessary. Previously, food analysis for phytoestrogens was performed using either HPLC-UV or GC/MS. Here, we describe the development of the first generic method for the analysis of phytoestrogens in food, using automated solid-phase extraction and liquid chromatography-tandem mass spectrometry. The presented method shows a good reproducibility and can be easily adapted to other phytoestrogens if required.     

Formation and decay of neutron—deficient nuclei Po and Pb

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Mean dynamic topography: inter-comparisons and errors

Knowledge of the ocean dynamic topography, defined as the height of the sea surface above its rest-state (the geoid), would allow oceanographers to study the absolute circulation of the ocean and determine the associated geostrophic surface currents that help to regulate the Earth's climate. Here a novel approach to computing a mean dynamic topography (MDT), together with an error field, is presented for the northern North Atlantic. The method uses an ensemble of MDTs, each of which has been produced by the assimilation of hydrographic data into a numerical ocean model, to form a composite MDT, and uses the spread within the ensemble as a measure of the error on this MDT. The r.m.s. error for the composite MDT is 3.2 cm, and for the associated geostrophic currents the r.m.s. error is 2.5 cms(-1). Taylor diagrams are used to compare the composite MDT with several MDTs produced by a variety of alternative methods. Of these, the composite MDT is found to agree remarkably well with an MDT based on the GRACE geoid GGM01C. It is shown how the composite MDT and its error field are useful validation products against which other MDTs and their error fields can be compared.     

Perceptual learning immediately yields new stable motor coordination.

Coordinated rhythmic movement is specifically structured in humans. Movement at 0° mean relative phase is maximally stable, 180° is less stable, and other coordinations can, but must, be learned. Variations in perceptual ability play a key role in determining the observed stabilities so we investigated whether stable movements can be acquired by improving perceptual ability. We assessed movement stability in Baseline, Post Training, and Retention sessions by having participants use a joystick to coordinate the movement of two dots on a screen at three relative phases. Perceptual ability was also assessed using a two-alternative forced choice task in which participants identified a target phase of 90° in a pair of displays. Participants then trained with progressively harder perceptual discriminations around 90° with feedback. Improved perceptual discrimination of 90° led to improved performance in the movement task at 90° with no training in the movement task. The improvement persisted until Retention without further exposure to either task. A control group's movement stability did not improve. Movement stability is a function of perceptual ability, and information is an integral part of the organization of this dynamical system. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Highly Active Dinuclear Copper Catalysts for Homogeneous Azide–Alkyne Cycloadditions

The main feature of the herein presented class of molecularly defined catalysts for the copper‐catalyzed azide–alkyne cycloaddition reaction is the presence of two copper centres in one catalyst molecule. We report the facile three‐step synthesis of two representative bis‐NHC‐dicopper complexes as well as their catalytic performance in the azide–alkyne cycloaddition. A screening with one of these complexes has proved its wide applicability and excellent performance as homogeneous catalyst in various organic solvents and with different alkyne and azide substrates.     

Developing an Updated Strategy for Estimating the Free-Energy Parameters in RNA Duplexes

For the last 20 years, it has been common lore that the free energy of RNA duplexes formed from canonical Watson–Crick base pairs (bps) can be largely approximated with dinucleotide bp parameters and a few simple corrective constants that are duplex independent. Additionally, the standard benchmark set of duplexes used to generate the parameters were GC-rich in the shorter duplexes and AU-rich in the longer duplexes, and the length of the majority of the duplexes ranged between 6 and 8 bps. We were curious if other models would generate similar results and whether adding longer duplexes of 17 bps would affect the conclusions. We developed a gradient-descent fitting program for obtaining free-energy parameters—the changes in Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS), and the melting temperature (Tm)—directly from the experimental melting curves. Using gradient descent and a genetic algorithm, the duplex melting results were combined with the standard benchmark data to obtain bp parameters. Both the standard (Turner) model and a new model that includes length-dependent terms were tested. Both models could fit the standard benchmark data; however, the new model could handle longer sequences better. We developed an updated strategy for fitting the duplex melting data.