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In order to explore the consequences of spin–orbit coupling on spin–phonon interactions in a set of chemically similar mixed metal oxides, we measured the infrared vibrational properties of Co4B2O9 (B = Nb, Ta) as a function of temperature and compared our findings with lattice dynamics calculations and several different models of spin–phonon coupling. Frequency vs temperature trends for the Co2+ shearing mode near 150 cm−1 reveal significant shifts across the magnetic ordering temperature that are especially large in relative terms. Bringing these results together and accounting for noncollinearity, we obtain spin–phonon coupling constants of −3.4 and −4.3 cm−1 for Co4Nb2O9 and the Ta analog, respectively. Analysis reveals that these coupling constants are derived from interlayer (rather than intralayer) exchange interactions and that the interlayer interactions contain competing antiferromagnetic and ferromagnetic contributions. At the same time, beyond-Heisenberg terms are minimized due to fortuitous symmetry considerations, different from most other 4d- and 5d-containing oxides. Comparison with other contemporary oxides shows that spin–phonon coupling in this family of materials is among the strongest ever reported, suggesting an origin for magnetoelectric coupling. 

Modular construction aims at overcoming challenges faced by the traditional construction process such as the shortage of skilled workers, fast-track project requirements, and cost associated with on-site productivity losses and recurrent rework. Since manufacturing is done off-site in controlled factory settings, modular construction is associated with increased productivity and better quality control. However, because every construction project is unique and results in distinct work pieces and building elements to be assembled, modular construction factories necessitate better mechanisms to assist workers during the assembly process in order to minimize errors in selecting the pieces to be assembled and idle times while figuring out the next step in an assembly sequence. Machine intelligence provides opportunities for such assistance; however, a challenge is to rapidly generate large datasets with rich contextual data to train such intelligent agents. This work overviews a mechanism to generate such datasets in virtual environments and evaluates the performance of AI models trained using data generated in virtual environments in recognizing the next installation step in modular assembly sequences. Performance of the trained MV-CNN models (with accuracy of 0.97) shows that virtual environments can potentially be used to generate the required datasets for AI without the costly, time-consuming, and labor-intensive investments needed upfront for capturing real-world data. 

OBJECTIVES: Prediction and determination of drug efficacy for radiographic progression is limited by the heterogeneity inherent in axial spondyloarthritis (axSpA). We investigated whether unbiased clustering analysis of phenotypic data can lead to coherent subgroups of axSpA patients with a distinct risk of radiographic progression. METHODS: A group of 412 patients with axSpA was clustered in an unbiased way using a agglomerative hierarchical clustering method, based on their phenotype mapping. We used a generalised linear model, naïve Bayes, Decision Trees, K-Nearest-Neighbors, and Support Vector Machines to construct a consensus classification method. Radiographic progression over 2 years was assessed using the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). RESULTS: axSpA patients were classified into three distinct subgroups with distinct clinical characteristics. Sex, smoking, HLA-B27, baseline mSASSS, uveitis, and peripheral arthritis were the key features that were found to stratifying the phenogroups. The three phenogroups showed distinct differences in radiographic progression rate (p<0.05) and the proportion of progressors (p<0.001). Phenogroup 2, consisting of male smokers, had the worst radiographic progression, while phenogroup 3, exclusively suffering from uveitis, showed the least radiographic progression. The axSpA phenogroup classification, including its ability to stratify risk, was successfully replicated in an independent validation group. CONCLUSIONS: Phenotype mapping results in a clinically relevant classification of axSpA that is applicable for risk stratification. Novel coupling between phenotypic features and radiographic progression can provide a glimpse into the mechanisms underlying divergent and shared features of axSpA. 

Abstract The anomalous diffusion of resonant protons in parallel and perpendicular velocity space by kinetic Alfvén waves is discussed. The velocity diffusion coefficient is calculated by employing an autocorrelation function for proton trajectories. It is found that for protons resonant with the waves, the perpendicular diffusion coefficient decays away for a sufficiently long time, but parallel diffusion monotonically increases in time until it saturates at a certain level. This result indicates that a portion of resonant protons can undergo anomalous diffusion along the background magnetic field even if the intensity of the kinetic Alfvén wave is sufficiently low. The present findings imply that under suitable conditions, astrophysical charged-particle acceleration can take place in the parallel direction.