Screening of magnetic fields by superconducting and hybrid shields with circular cross-section

The use of superconducting (SC) materials is crucial for shielding quasi-static magnetic fields. However, the frequent requisite of space-saving solutions with high shielding performance requires the development of a 3D modelling procedure capable of predicting the screening properties for different orientations of the applied field. In this paper, we exploited a 3D numerical model based on a vector potential formulation to investigate the shielding ability of SC screens with cylindrical symmetry and a height/diameter aspect ratio close to unity, without and with the superimposition of a ferromagnetic (FM) circular shell. The chosen materials were MgB2 and soft iron. First, the calculation outcomes were compared with the experimental data obtained on different shielding arrangements, achieving a notable agreement in both axial-field (AF) and transverse-field (TF) orientations. Then, we used the thus validated modelling approach to investigate how the magnetic mitigation properties of a cup-shaped SC bulk can be improved by the superimposition of a coaxial FM cup. Calculations highlighted that the FM addition is very efficient in enhancing the shielding factors (SFs) in the TF orientation. Assuming a working temperature of 30 K and using a layout with the FM cup protruding over the SC one, shielding factors up to 8 times greater than those of the single SC cup were attained at low applied fields, reaching values equal or higher than 102 in the inner half of the shield. In the AF orientation, the same FM cup addition costs a modest worsening at low fields, but at the same time, it widens the applied field range, where SF ≥ 104 occurs near the close extremity of the shield, up over 1 T.

Averting the next credibility crisis in psychological science: Within-person methods for personalized diagnostics and intervention.

Personalizing assessments, predictions, and treatments of individuals is currently a defining trend in psychological research and applied fields, including personalized learning, personalized medicine, and personalized advertisement. For instance, the recent pandemic has reminded parents and educators of how challenging yet crucial it is to get the right learning task to the right student at the right time. Increasingly, psychologists and social scientists are realizing that the between- person methods that we have long relied upon to describe, predict, and treat individuals may fail to live up to these tasks (e.g., Molenaar, 2004). Consequently, there is a risk of a credibility loss, possibly similar to the one seen during the replicability crisis (Ioannides, 2005), because we have only started to understand how many of the conclusions that we tend to draw based on between-person methods are based on a misunderstanding of what these methods can tell us and what they cannot. An imminent methodological revolution will likely lead to a change of even well-established psychological theories (Barbot et al., 2020). Fortunately, methodological solutions for personalized descriptions and predictions, such as many within-person analyses, are available and undergo rapid development, although they are not yet embraced in all areas of psychology, and some come with their own limitations. This article first discusses the extent of the theory-method gap, consisting of theories about within-person patterns being studied with between-person methods in psychology, and the potential loss of trust that might follow from this theory-method gap. Second, this article addresses advantages and limitations of available within- person methods. Third, this article discusses how within-person methods may help improving the individual descriptions and predictions that are needed in many applied fields that aim for tailored individual solutions, including personalized learning and personalized medicine.

NMR Relaxivities of Paramagnetic Lanthanide-Containing Polyoxometalates

The current trend for ultra-high-field magnetic resonance imaging (MRI) technologies opens up new routes in clinical diagnostic imaging as well as in material imaging applications. MRI selectivity is further improved by using contrast agents (CAs), which enhance the image contrast and improve specificity by the paramagnetic relaxation enhancement (PRE) mechanism. Generally, the efficacy of a CA at a given magnetic field is measured by its longitudinal and transverse relaxivities r1 and r2, i.e., the longitudinal and transverse relaxation rates T1−1 and T2−1 normalized to CA concentration. However, even though basic NMR sensitivity and resolution become better in stronger fields, r1 of classic CA generally decreases, which often causes a reduction of the image contrast. In this regard, there is a growing interest in the development of new contrast agents that would be suitable to work at higher magnetic fields. One of the strategies to increase imaging contrast at high magnetic field is to inspect other paramagnetic ions than the commonly used Gd(III)-based CAs. For lanthanides, the magnetic moment can be higher than that of the isotropic Gd(III) ion. In addition, the symmetry of electronic ground state influences the PRE properties of a compound apart from diverse correlation times. In this work, PRE of water 1H has been investigated over a wide range of magnetic fields for aqueous solutions of the lanthanide containing polyoxometalates [DyIII(H2O)4GeW11O39]5– (Dy-W11), [ErIII(H2O)3GeW11O39]5– (Er-W11) and [{ErIII(H2O)(CH3COO)(P2W17O61)}2]16− (Er2-W34) over a wide range of frequencies from 20 MHz to 1.4 GHz. Their relaxivities r1 and r2 increase with increasing applied fields. These results indicate that the three chosen POM systems are potential candidates for contrast agents, especially at high magnetic fields.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues

BackgroundThe ability to quantify the colour of fruit is extremely important for a number of applied fields including plant breeding, postharvest assessment, and consumer quality assessment. Fruit and other plant organs display highly complex colour patterning. This complexity makes it challenging to compare and contrast colours in an accurate and time efficient manner. Multiple methodologies exist that attempt to digitally quantify colour in complex images but these either require a priori knowledge to assign colours to a particular bin, or average the colours present within an assayed region into a single colour value. As such, to date there are no published methodologies that assess colour patterning using a data driven approach. Results In this study we present a methodology to acquire and process digital images of biological samples that contain complex colour gradients. The CIE (Commission internationale de l'éclairage / International Commission on Illumination) ΔE2000 formula was used to determine the perceptually unique colours (PUC) within images of fruit containing complex colour gradients. This process, on average, resulted in a 98% reduction in colour values from the number of unique colours (UC) in the original image. This data driven procedure summarised the colour data values while maintaining a linear relationship with the normalised colour complexity contained in the total image. A weighted ΔE2000 distance metric was used to generate a distance matrix and facilitated clustering of summarised colour data.ConclusionsClustering showed that our data driven methodology has the ability to group these complex images into their respective binomial families while maintaining the ability to detect subtle colour differences. This methodology was also able to differentiate closely related images. We provide a high quality set of complex biological images that span the visual spectrum that can be used in future colorimetric research to benchmark method development.

Localization of social science research in selected academic disciplines in South Korea

Social science contributes to social development when theory and research topic are linked to its social context. However, in practice most social scientists in South Korea tend to explain their social issues and problems through mainstream theoretical perspectives that were primarily developed in the West. This study investigates how much social science research is localized in four selected social science disciplines (sociology, political science, public administration, and education) in South Korea. The study analyzes articles published in one representative domestic journal in each discipline to assess the localization of knowledge production during the last three decades (1988–2017). It was found that the local knowledge-base of Korean social science research is relatively weak though it has been continuously increasing during the last three decades. It was also found that knowledge production in social sciences is reliant on Western theory even though the research topics are locally embedded. In addition, the findings revealed that there are noticeable differences between the applied fields (public administration and education) and the pure fields (sociology and political science). Applied fields of public administration and education are more locally embedded than pure fields of sociology and political science. This study proposes that social science research in South Korea should draw more on indigenous knowledge and be less reliant on Western theory in the future.

Slopes: solute processes and landforms

This chapter reviews research on solutes by fluvial geomorphologists in the period 1965 to 2000; growing links with biogeochemical research are emphasised later in the chapter. Brief reference is necessarily made to some research from before and after the study period. In relation to solutes, early research sought to relate short-term process observations to long-term landform evolution. However, very quickly, research moved into much more applied fields, less concerned with landforms and more with biogeochemical processes. The drainage basin became the focus of research with a wide range of interest including nutrient loss from agricultural and forested landscapes to dissolved organic carbon export from peatlands. In particular, the terrestrial-aquatic ecotone became a focus for research, emphasising the distinctive processes operating in the riparian zone and their contribution to river water protection from land-derived pollutants. By the end of the period, the scale and range of fluvial geomorphology had been greatly transformed from what it had been in 1965, providing a distinctive contribution to the broader field of biogeochemistry as well as an ongoing contribution to the study of Earth surface processes and landforms.

Optimization of VQE-UCC Algorithm Based on Spin State Symmetry

The accurate calculation of molecular energy spectra, a very complicated work, is of importance in many applied fields. Relying on the VQE-UCC algorithm, it is very possible to calculate the molecular energy spectrum on a noisy intermediate scale quantum computer. However, due to the limitation of the number of qubits and coherent time in quantum computers, the complexity of VQE-UCC algorithm still needs to be reduced in the simulation of macromolecules. We develop a new VQE-UCC method to calculate the ground state of the molecule according to the symmetry of the system, the complexity of which is reduced. Using this method we get the ground and excite state of four kinds of molecules. The method and the results are of great significance for the promotion of quantum chemical simulations.

Structural and Electrical Characteristics of FeTiVO6 Double Perovskite

In this paper, studies of structural as well as electrical characteristics of the double perovskite material FeTiVO6 (iron titanium vanadate), synthesized by a high-temperature mixed oxide reaction method have been discussed. The room temperature X-ray diffraction analysis confirms the formation of a single-phase orthorhombic structure without any secondary phase. All the electrical characteristics (i.e., dielectric, impedance, conductivity and modulus) of the sample, studied at various temperatures (25–300°C) and frequencies (1[Formula: see text]kHz–1[Formula: see text]MHz), provide many remarkable characteristics of the material. The dielectric parameters as a function of frequency explain the presence of different polarization mechanisms based on the Maxwell–Wagner double-layer model. Impedance analysis describes the grain (bulk) and grain boundary (bulk interior) effect on the material using the equivalent RQC-RC circuits. The presence of non-Debye type of relaxation behavior in the material is confirmed by the depressed semicircles of Nyquist plots. The conductivity study provides information about the CBH and OLPT type of conduction phenomenon. The temperature dependence of leakage current behavior follows the Ohmic (semiconductor) and space charge limited conduction mechanisms at a different range of applied fields. The occurrence of the room temperature hysteresis loop obtained from the PE loop tracer confirms the ferroelectric behavior of the studied compound.

Facile fabrication and nanoscale assembly of polydopamine-functionalized, flexible chitosan films

Substrates that are simultaneously thin, strong, optically transparent, and biocompatible have diverse applications in a range of fundamental and applied fields. While nature-derived materials offer advantages of sustainability and inherent biocompatibility compared to synthetic polymers, their brittleness and swelling, as well as surface charge and chemical functionalization non-conducive to cell growth, can hinder widespread application. In this work, we discuss the fabrication and systematic characterization of polydopamine-coated chitosan thin films. Chitosan is a widely used, partially deacetylated form of chitin, derived from crustaceans and arthropods. Polydopamine (PDA) is derived from chemistries mimicking mussel foot adhesive proteins. A facile dip-coating process of thin and flexible, uncrosslinked chitosan films in aqueous dopamine solutions leads to dramatic changes in physical and chemical properties. We show how the PDA forms time-dependent assemblies on the film surfaces, affecting surface roughness, hydrophilicity, and mechanical strength. Coating the surface for even a few seconds provides functional changes to the films. Our results shows that the optimal coating time is on the order of few hours, whereby the films are optically transparent with excellent extensibility and Young’s modulus, while further coating reduces the benefits of this surface coating. These materials are biocompatible, serving as substrates for cell adhesion and growth while maintaining good viability. Overall, these findings give insight to the effects of PDA assembly on surfaces, and illustrate how a simple, quick, and robust bioinspired coating process can prime substrates for biomedical applications such as tissue engineering, biosensing, and wound healing.