Influence of Mg, Ag and Al substitutions on the magnetic excitations in the triangular-lattice antiferromagnet CuCrO2

Magnetic excitations in CuCrO$_{2}$, CuCr$_{0.97}$Mg$_{0.03}$O$_{2}$, Cu$_{0.85}$Ag$_{0.15}$CrO$_{2}$, and CuCr$_{0.85}$Al$_{0.15}$O$_{2}$ have been studied by powder inelastic neutron scattering to elucidate the element substitution effects on the spin dynamics in the Heisenberg triangular-lattice antiferromagnet CuCrO$_{2}$. The magnetic excitations in CuCr$_{0.97}$Mg$_{0.03}$O$_{2}$ consist of a dispersive component and a flat component. Though this feature is apparently similar to CuCrO$_{2}$, the energy structure of the excitation spectrum shows some difference from that in CuCrO$_{2}$. On the other hand, in Cu$_{0.85}$Ag$_{0.15}$CrO$_{2}$ and CuCr$_{0.85}$Al$_{0.15}$O$_{2}$ the flat components are much reduced, the low-energy parts of the excitation spectra become intense, and additional low-energy diffusive spin fluctuations are induced. We argued the origins of these changes in the magnetic excitations are ascribed to effects of the doped holes or change of the dimensionality in the magnetic correlations.Comment: 7 pages, 5 figure

A Two-dimensional Infinte System Density Matrix Renormalization Group Algorithm

It has proved difficult to extend the density matrix renormalization group technique to large two-dimensional systems. In this Communication I present a novel approach where the calculation is done directly in two dimensions. This makes it possible to use an infinite system method, and for the first time the fixed point in two dimensions is studied. By analyzing several related blocking schemes I find that there exists an algorithm for which the local energy decreases monotonically as the system size increases, thereby showing the potential feasibility of this method.Comment: 5 pages, 6 figure

The critical behaviour of the 2D Ising model in Transverse Field; a Density Matrix Renormalization calculation

We have adjusted the Density Matrix Renormalization method to handle two dimensional systems of limited width. The key ingredient for this extension is the incorporation of symmetries in the method. The advantage of our approach is that we can force certain symmetry properties to the resulting ground state wave function. Combining the results obtained for system sizes up-to $30 \times 6$ and finite size scaling, we derive the phase transition point and the critical exponent for the gap in the Ising model in a Transverse Field on a two dimensional square lattice.Comment: 9 pages, 8 figure

The 1/D Expansion for Classical Magnets: Low-Dimensional Models with Magnetic Field

The field-dependent magnetization m(H,T) of 1- and 2-dimensional classical magnets described by the $D$-component vector model is calculated analytically in the whole range of temperature and magnetic fields with the help of the 1/D expansion. In the 1-st order in 1/D the theory reproduces with a good accuracy the temperature dependence of the zero-field susceptibility of antiferromagnets \chi with the maximum at T \lsim |J_0|/D (J_0 is the Fourier component of the exchange interaction) and describes for the first time the singular behavior of \chi(H,T) at small temperatures and magnetic fields: \lim_{T\to 0}\lim_{H\to 0} \chi(H,T)=1/(2|J_0|)(1-1/D) and \lim_{H\to 0}\lim_{T\to 0} \chi(H,T)=1/(2|J_0|)

A Computation of the Maximal Order Type of the Term Ordering on Finite Multisets

We give a sharpening of a recent result of Aschenbrenner and Pong about the maximal order type of the term ordering on the finite multisets over a wpo. Moreover we discuss an approach to compute maximal order types of well-partial orders which are related to tree embeddings

A density matrix renormalisation group algorithm for quantum lattice systems with a large number of states per site

A variant of White's density matrix renormalisation group scheme which is designed to compute low-lying energies of one-dimensional quantum lattice models with a large number of degrees of freedom per site is described. The method is tested on two exactly solvable models---the spin-1/2 antiferromagnetic Heisenberg chain and a dimerised XY spin chain. To illustrate the potential of the method, it is applied to a model of spins interacting with quantum phonons. It is shown that the method accurately resolves a number of energy gaps on periodic rings which are sufficiently large to afford an accurate investigation of critical properties via the use of finite-size scaling theory.Comment: RevTeX, 8 pages, 2 figure

Tissue-specific expression of high-voltage-activated dihydropyridine-sensitive L-type calcium channels

The cloning of the cDNA for the α1 subunit of L-type calcium channels revealed that at least two genes (CaCh1 and CaCh2) exist which give rise to several splice variants. The expression of mRNA for these α1 subunits and the skeletal muscle α2/δ, β and γ subunits was studied in rabbit tissues and BC3H1 cells. Nucleic-acid-hybridization studies showed that the mRNA of all subunits are expressed in skeletal muscle, brain, heart and aorta. However, the α1-, β- and γ-specific transcripts had different sizes in these tissues. Smooth muscle and heart contain different splice variants of the CaCh2 gene. The α1, β and γ mRNA are expressed together in differentiated but not in proliferating BC3H1 cells. A probe specific for the skeletal muscle α2/δ subunit did not hybridize to poly(A)-rich RNA from BC3H1 cells. These results suggest that different splice variants of the genes for the α1, β and γ subunits exist in tissues containing L-type calcium channels, and that their expression is regulated in a coordinate manner

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

Objective: To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. Background: RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. Methods: Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. Results: The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. Conclusion: This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.</p