Manipulating non-Hermitian skin effect via electric fields

In non-Hermitian systems, the phenomenon that the bulk-band eigenstates are accumulated at the boundaries of the systems under open boundary conditions is called non-Hermitian skin effect (NHSE), which is one of the most iconic and important features of a non-Hermitian system. In this work, we investigate the fate of NHSE in the presence of electric fields by analytically calculating the dynamical evolution of an initial bulk state and numerically computing the spectral winding number, the distributions of eigenstates, as well as the dynamical evolutions. We show abundant manipulation effects of dc and ac fields on the NHSE, and that the physical mechanism behind these effects is the interplay between the Stark localization, dynamic localization and the NHSE. In addition, the finite size analysis of the non-Hermitian system with a pure dc field shows the phenomenon of size-dependent NHSE. We further propose a scheme to realize the discussed model based on an electronic circuit. The results will help to deepen the understanding of NHSE and its manipulation

Dissipation induced extended-localized transition

Mobility edge (ME), representing the critical energy that distinguishes between extended and localized states, is a key concept in understanding the transition between extended (metallic) and localized (insulating) states in disordered and quasiperiodic systems. Here we explore the impact of dissipation on a quasiperiodic system featuring MEs by calculating steady-state density matrix and analyzing quench dynamics with sudden introduction of dissipation, and demonstrate that dissipation can lead the system into specific states predominantly characterized by either extended or localized states, irrespective of the initial state. Our results establish the use of dissipation as a new avenue for inducing transitions between extended and localized states, and for manipulating dynamic behaviors of particles

Investigation of countercurrent flow profile and liquid holdup in random packed column with local CFD data

Liquid holdup and mass transfer area are critical parameters for packed column design and CO2 capture efficiency prediction. In this paper, a framework was established for modeling the liquid-gas countercurrent flow hydrodynamics in a random packed column with pall rings. Besides the column-averaged information, the radial pall ring distribution, velocity, and liquid holdup profiles are obtained to study the entrance effect and the wall influence in the packed column. With local CFD data, the validated packing specific area ap and liquid velocity uL range for liquid holdup correlation is significantly expanded with respect to existing experimental or column-averaged CFD data. The proposed liquid holdup correlation $h_L \propto u_L^{0.44}$ indicates the random packed column falls in a viscous to turbulent transition regime and it covers a Reynolds Number range of [6.7-40.2]. The derived liquid holdup correlation is in good agreement with existing correlations developed using the column-averaged experimental data

Response of lignin and flavonoid metabolic pathways in Capsicum annuum to drought and waterlogging stresses

Water stress is a critical factor limiting the growth and development of Capsicum annuum. Flavonoids and lignin are important secondary metabolites that serve as signaling molecules in plant stress responses. However, the effects and regulatory mechanisms of lignin and flavonoids under water stress in Capsicum annuum remain unknown. The present study focused on the effects of drought and waterlogging stress on the morphology, hydrogen peroxide, and relative chlorophyll (SPAD), as well as enzyme activities, metabolite contents, and gene expression related to lignin and flavonoid metabolic pathways in Capsicum annuum. The results showed that drought and waterlogging stresses on the Capsicum annuum variety ‘Shuyu2’ significantly reduced plant height, stem thickness, and single-fruit weight, and increased fruit shape coefficients. Drought stress increased H2O2 and SPAD content, enhanced the activity levels of metabolic enzymes (phenylalanine deaminase, cinnamate 4-hydroxylase, coenzyme A ligase, peroxidase, and polyphenol oxidase), and up-regulated the expression of related genes, phenylalanine deaminase (PAL), trans-cinnamate monooxygenase (C4H), chalcone isomerase (CHI), and mangiferyl hydroxycinnamoyltransferase (HCT), while also promoting the accumulation of metabolites (total phenolics, flavonoids, and lignin) that have a restorative effect on drought stress. The continuous accumulation of H2O2 and the increase and then decrease in SPAD under waterlogging stress was also observed. Waterlogging stress also enhanced the activities of the above-mentioned metabolic enzymes, but the related genes were selectively down-regulated, e.g., C4H, 4CL, and peroxidase (POD), which resulted in the inhibition of the synthesis of lignin, flavonoids, and total phenols. These results indicate that the Capsicum annuum variety ‘Shuyu2’ is a drought-tolerant, waterlogging-sensitive variety. Meanwhile, the lignin and flavonoid pathway is a key pathway in response to drought stress in Capsicum annuum, which improves the theory of stress tolerance breeding in Capsicum annuum

Encoder-Decoder-Based Intra-Frame Block Partitioning Decision

The recursive intra-frame block partitioning decision process, a crucial component of the next-generation video coding standards, exerts significant influence over the encoding time. In this paper, we propose an encoder-decoder neural network (NN) to accelerate this process. Specifically, a CNN is utilized to compress the pixel data of the largest coding unit (LCU) into a fixed-length vector. Subsequently, a Transformer decoder is employed to transcribe the fixed-length vector into a variable-length vector, which represents the block partitioning outcomes of the encoding LCU. The vector transcription process adheres to the constraints imposed by the block partitioning algorithm. By fully parallelizing the NN prediction in the intra-mode decision, substantial time savings can be attained during the decision phase. The experimental results obtained from high-definition (HD) sequences coding demonstrate that this framework achieves a remarkable 87.84\% reduction in encoding time, with a relatively small loss (8.09\%) of coding performance compared to AVS3 HPM4.0

Accelerating Relaxation Dynamics in Open Quantum System with Liouvillian Skin Effect

We investigate a non-Hermitian model featuring non-reciprocal gradient hoppings. Through an in-depth analysis of the Liouvillian spectrum and dynamics, we confirm the emergence of the Liouvillian skin effect resulting from the non-reciprocal nature of hoppings in this model. Furthermore, we observe that the presence of gradient hopping strength leads to an accelerated relaxation time for the system. Through numerical investigations of the Liouvillian gap, relaxation time, and steady-state localization length, we discover that the relaxation time in this model cannot be explained by the currently established relationship associated with the Liouvillian skin effect. This discrepancy highlights the need for further exploration and theoretical advancements to fully comprehend the intricate mechanisms underlying quantum relaxation processes. Motivated by these findings, we propose a theoretical approach to realize this non-Hermitian model in an atomic system with a sideband structure by employing adiabatic elimination technique. These results contribute to our deeper comprehension of quantum relaxation dynamics and provide theoretical backing for the development of techniques aimed at controlling quantum relaxation processes.Comment: 9 pages, 6 figures, To be published in PR

Orthopedic Center of Chinese PLA, Urumqi General Hospital of Lanzhou Military Region,

Sphingosine-1-phosphate is a possible fibrogenic factor in glutea

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6x faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio