The rare semi-leptonic BcB_c decays involving orbitally excited final mesons

The rare processes $B_c\to D_{(s)J} ^{(*)}\mu\bar{\mu}$, where $D_{(s)J}^{(*)}$ stands for the final meson $D_{s0}^*(2317)$, $D_{s1}(2460,2536)$,~$D_{s2}^*(2573)$, $D_0^*(2400)$, $D_{1}(2420,2430)$ or~$D_{2}^*(2460)$, are studied within the Standard Model. The hadronic matrix elements are evaluated in the Bethe-Salpeter approach and furthermore a discussion on the gauge-invariant condition of the annihilation hadronic currents is presented. Considering the penguin, box, annihilation, color-favored cascade and color-suppressed cascade contributions, the observables $\text{d}Br/\text{d}Q^2$, $A_{LPL}$, $A_{FB}$ and $P_L$ are calculated

rac-4-[4-Cyano-2-(hy­droxy­meth­yl)phen­yl]-4-(4-fluoro­phen­yl)-4-hy­droxy-N,N-dimethyl­butanaminium hemifumarate

In the title salt, C20H24FN2O2 +·0.5C4H2O4 2−, the fumarate anion is located on an inversion centre. In the cation, the two benzene rings are nearly perpendicular to each other, making a dihedral angle of 87.41 (10)°. The cation is linked to the anion by a bifurcated N—H⋯O hydrogen bond. Classical O—H⋯O and weak C—H⋯F hydrogen bonding is also present in the crystal structure. Three C atoms of the N,N-dimethyl­butanaminium moiety are disordered over two sites with refined site occupancies of 0.466 (14) and 0.534 (14)

An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error

Constructing a Computer Model of the Human Eye Based on Tissue Slice Images

Computer simulation of the biomechanical and biological heat transfer in ophthalmology greatly relies on having a reliable computer model of the human eye. This paper proposes a novel method on the construction of a geometric model of the human eye based on tissue slice images. Slice images were obtained from an in vitro Chinese human eye through an embryo specimen processing methods. A level set algorithm was used to extract contour points of eye tissues while a principle component analysis was used to detect the central axis of the image. The two-dimensional contour was rotated around the central axis to obtain a three-dimensional model of the human eye. Refined geometric models of the cornea, sclera, iris, lens, vitreous, and other eye tissues were then constructed with their position and ratio relationships kept intact. A preliminary study of eye tissue deformation in eye virtual surgery was simulated by a mass-spring model based on the computer models developed

Bis[4-(2-hy­droxy­benzyl­idene­amino)­benzoato-κO 1]tetra­kis­(methanol-κO)cadmium

In the title mononuclear complex, [Cd(C14H10NO3)2(CH3OH)4], the Cd2+ cation is situated on an inversion centre. It exhibits a distorted octa­hedral coordination, defined by two carboxyl­ate O atoms from two monodentate anions and by four O atoms from four methanol mol­ecules. The crystal structure comprises intra­molecular O—H⋯O and O—H⋯N, and inter­molecular O—H⋯O hydrogen bonds. The latter help to construct a layered structure extending parallel to (100)

Gender-specific association of MSA2756G with hypertension in patients attending a health facility in Ningxia Province, China

Purpose: To investigate the distribution of methionine synthase A2756G (MSA2756G) in the hypertensive patients in northwest Chinese population.Methods: A total of 378 unrelated hypertensive patients attending Ningxia Peoples Hospital, Ningxia Province, China, were recruited for this study. We analyzed genotype by amplication - created restriction sites (ACRS) and polymerase chain reaction - restrict fragment length polymorphism (PCR - RFLP) in hypertensive patients, and inspected the relation of the genotype with hypertension by χ2 and t test.Results: The frequency of G allele was 10.25 % in the control group and 14.04 % in hypertension group; it was not statistically different (p > 0.05). In the male group, the frequency of allele G was 11.50 % in control group, and 8.79 % in hypertension group. There was no significant difference between control and hypertension groups (p > 0.05). In the female group, the frequency of allele G was 9.00 %, in control and 19.54 % in hypertension group (p < 0.05), while in the hypertension group, allele G was 8.79 % in males which is significantly lower (p < 0.05) than in females (19.54 %) .Conclusion: Allele G of MSA2756G is a risk factor for hypertension in female in this Chinese population of this study.Keywords: Hypertension, Methionine synthase, Polymorphism, Gender, Amplification-created restriction sites, Allele G, MSA2756

An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error

Persistent Ballistic Entanglement Spreading with Optimal Control in Quantum Spin Chains

Entanglement propagation provides a key routine to understand quantum many-body dynamics in and out of equilibrium. In this work, we uncover that the ``variational entanglement-enhancing'' field (VEEF) robustly induces a persistent ballistic spreading of entanglement in quantum spin chains. The VEEF is time dependent, and is optimally controlled to maximize the bipartite entanglement entropy (EE) of the final state. Such a linear growth persists till the EE reaches the genuine saturation $\tilde{S} = - \log_{2} 2^{-\frac{N}{2}}=\frac{N}{2}$ with $N$ the total number of spins. The EE satisfies $S(t) = v t$ for the time $t \leq \frac{N}{2v}$, with $v$ the velocity. These results are in sharp contrast with the behaviors without VEEF, where the EE generally approaches a sub-saturation known as the Page value $\tilde{S}_{P} =\tilde{S} - \frac{1}{2\ln{2}}$ in the long-time limit, and the entanglement growth deviates from being linear before the Page value is reached. The dependence between the velocity and interactions is explored, with $v \simeq 2.76$, $4.98$, and $5.75$ for the spin chains with Ising, XY, and Heisenberg interactions, respectively. We further show that the nonlinear growth of EE emerges with the presence of long-range interactions.Comment: 5 pages, 4 figure

3-[4-(2-Amino-2-oxoeth­yl)phen­oxy]-2-hy­droxy-N-isopropyl­propanaminium 1,1′-binaphthyl-2,2′-diyl phosphate

In the title salt, C14H23N2O3 +·C20H12O4P−, the dihedral angle between the two naphthyl ring systems in the anion is 57.77 (6)°. In the crystal, an O—H⋯O hydrogen bond links the components. The ammonium group engages in N—H⋯O hydrogen bonds, generating a layer structure

BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion

Image inpainting, the process of restoring corrupted images, has seen significant advancements with the advent of diffusion models (DMs). Despite these advancements, current DM adaptations for inpainting, which involve modifications to the sampling strategy or the development of inpainting-specific DMs, frequently suffer from semantic inconsistencies and reduced image quality. Addressing these challenges, our work introduces a novel paradigm: the division of masked image features and noisy latent into separate branches. This division dramatically diminishes the model's learning load, facilitating a nuanced incorporation of essential masked image information in a hierarchical fashion. Herein, we present BrushNet, a novel plug-and-play dual-branch model engineered to embed pixel-level masked image features into any pre-trained DM, guaranteeing coherent and enhanced image inpainting outcomes. Additionally, we introduce BrushData and BrushBench to facilitate segmentation-based inpainting training and performance assessment. Our extensive experimental analysis demonstrates BrushNet's superior performance over existing models across seven key metrics, including image quality, mask region preservation, and textual coherence