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Breast cancer lesion segmentation in DCE-MRI remains challenging due to heterogeneous tumor morphology and indistinct boundaries. To address these challenges, this study proposes a novel hybrid segmentation network, HCMA-UNet, for lesion segmentation of breast cancer. Our network consists of a lightweight CNN backbone and a Multi-view Inter-Slice Self-Attention Mamba (MISM) module. The MISM module integrates Visual State Space Block (VSSB) and Inter-Slice Self-Attention (ISSA) mechanism, effectively reducing parameters through Asymmetric Split Channel (ASC) strategy to achieve efficient tri-directional feature extraction. Our lightweight model achieves superior performance with 2.87M parameters and 126.44 GFLOPs. A Feature-guided Region-aware loss function (FRLoss) is proposed to enhance segmentation accuracy. Extensive experiments on one private and two public DCE-MRI breast cancer datasets demonstrate that our approach achieves state-of-the-art performance while maintaining computational efficiency. FRLoss also exhibits good cross-architecture generalization capabilities. The source code and dataset is available on this link.

Several pulsar timing array (PTA) groups have recently claimed the detection of nanohertz gravitational wave (GW) background, but the origin of this GW signal remains unclear. Nanohertz GWs generated by supermassive binary black holes (SMBBHs) are one of the most important GW sources in the PTA band. Utilizing data from numerical cosmology simulation, we generate mock SMBBHs within the observable universe and treat them as PTA band GW sources. We present their statistical properties, and analyze the isotropic and anisotropic characteristics of the gravitational wave background (GWB) signal they produce. Specifically, we derive the characteristic amplitude and spectrum of the GWB signal, and calculate the angular power spectrum for both GW strains/energy density and the position distribution of GW sources. We predict that the angular power spectrum of GWB energy density has $C_1/C_0\approx0.40\pm0.32$, and $C_l/C_0\simeq \frac{1}{2(2l+1)}$ (for $l>1$). Furthermore, for the upcoming Chinese Pulsar Timing Array (CPTA) and Square Kilometre Array (SKA) PTA, we predict the spatial distribution, numbers and signal-to-noise ratio (SNR) distribution of individual GW sources that may be detected with SNR>8, and study the anisotropy property in the spatial distribution of these individual GW sources., Comment: 26 pages,18 figures,1 table

The direct CP asymmetry in the weak decay process of hadrons is commonly attributed to the weak phase of the CKM matrix and the indeterminate strong phase. We propose a way of creating a strong phase difference between two decay paths involving vector mesons $V= \omega,\rho$ decaying to $\pi^{+}\pi^{-}\pi^{0}$ considering the G-parity suppressed decay process $\rho^{0} \rightarrow \pi^{+}\pi^{-}\pi^{0} $. It can lead to a significant CP asymmetry in the interference region. We consider the effects of $\omega$ and $\rho$ mixing on their decays to $\pi^{+}\pi^{-}\pi^{0}$. Meanwhile, we calculate the CP asymmetry for the process $B\rightarrow \pi^{+}\pi^{-}\pi^{0}P$ (P refers to pseudoscalar meson) through $\omega(\rho) \rightarrow \pi^{+}\pi^{-}\pi^{0}$. Besides, we also calculate the integral results of different phase space regions. We hope that our work can help provide some theoretical guidance for future experimental studies of CP asymmetry in these decays.

Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models. Moreover, these models exhibit a bias for contrast-based images, with diminished performance on texture-based medical images. This study introduces the notion of a population-level context for pathology detection and employs a graph theoretic approach to model and incorporate it into the latent code of an autoencoder via a refinement module we term PopuSense. PopuSense seeks to capture additional intra-group variations inherent in biomedical data that a local or global context of the convolutional model might miss or smooth out. Proof-of-concept experiments on contrast-based and texture-based images, with minimal adaptation, encounter the existing preference for intensity-based input. Nevertheless, PopuSense demonstrates improved separability in contrast-based images, presenting an additional avenue for refining representations learned by a model.

The timely identification of mental disorders in adolescents is a global public health challenge.Single factor is difficult to detect the abnormality due to its complex and subtle nature. Additionally, the generalized multimodal Computer-Aided Screening (CAS) systems with interactive robots for adolescent mental disorders are not available. Here, we design an android application with mini-games and chat recording deployed in a portable robot to screen 3,783 middle school students and construct the multimodal screening dataset, including facial images, physiological signs, voice recordings, and textual transcripts.We develop a model called GAME (Generalized Model with Attention and Multimodal EmbraceNet) with novel attention mechanism that integrates cross-modal features into the model. GAME evaluates adolescent mental conditions with high accuracy (73.34%-92.77%) and F1-Score (71.32%-91.06%).We find each modality contributes dynamically to the mental disorders screening and comorbidities among various mental disorders, indicating the feasibility of explainable model. This study provides a system capable of acquiring multimodal information and constructs a generalized multimodal integration algorithm with novel attention mechanisms for the early screening of adolescent mental disorders.

We introduce the new resonance of $V\rightarrow K^{+}K^{-}$ $(V=\phi, \rho, \omega)$, which produces some new strong phase associated with vector meson resonance and thus can cause relatively large CP asymmetry at the range of interferences. There are the resonances of $\phi \rightarrow K^{+}K^{-}$, $\rho \rightarrow K^{+}K^{-}$ and $\omega \rightarrow K^{+}K^{-}$ due to the mixing of vector mesons $\phi$, $\rho$, $\omega$. We calculate the CP asymmetry from the decay modes of $B \rightarrow KK\pi(K)$. Meanwhile, the localised CP asymmetries are presented and some detailed analysis can be found. The CP asymmetry from the decay mode of ${B}^{-}\rightarrow \phi\pi^{-}\rightarrow K^{+}K^{-}\pi^{-}$ is also presented in our framework which is well consisted with LHC experiment. The introduced CP asymmetry can provide a favorable theoretical support for the experimental exploration in the future.

Aerodynamics plays an important role in aviation industry and aircraft design. Detecting and minimizing the phenomenon of flow separation from scattered pressure data on airfoil is critical for ensuring stable and efficient aviation. However, since it is challenging to understand the mechanics of flow field separation, the aerodynamic parameters are emphasized for the identification and control of flow separation. It has been investigated extensively using traditional algorithms and machine learning methods such as the support vector machine (SVM) models. Recently, a growing interest in quantum computing and its applications among wide research communities sheds light upon the use of quantum techniques to solve aerodynamic problems. In this paper, we apply qSVM, a quantum SVM algorithm based on the quantum annealing model, to identify whether there is flow separation, with their performance in comparison to the widely-used classical SVM. We show that our approach outperforms the classical SVM with an 11.1% increase of the accuracy, from 0.818 to 0.909, for this binary classification task. We further develop multi-class qSVMs based on one-against-all algorithm. We apply it to classify multiple types of the attack angles to the wings, where the advantage over the classical multi-class counterpart is maintained with an accuracy increased from 0.67 to 0.79, by 17.9%. Our work demonstrates a useful quantum technique for classifying flow separation scenarios, and may promote rich investigations for quantum computing applications in fluid dynamics., Comment: 12 pages, 4 figures, 3 supplementary figures

Single cesium lead bromide (CsPbBr3) nanocrystals show strong photoluminescence blinking, with on- and off- dwelling times following power-law distributions. We investigate the memory effect in the photoluminescence blinking of single CsPbBr3 nanocrystals and find positive correlations for successive on-times and successive off-times. This memory effect is not sensitive to the nature of the surface capping ligand and the embedding polymer. These observations suggest that photoluminescence intermittency and its memory are mainly controlled by intrinsic traps in the nanocrystals. These findings will help optimizing light-emitting devices based on inorganic perovskite nanocrystals.

We discuss the government's reward and penalty mechanism in the presence of asymmetric information and carbon emission constraint when downstream retailers compete in a reverse supply chain network. Considering five game models which are different in terms of the coordination structure of the reverse supply chain network and power structure of the reward-penalty mechanism: (1) the reverse supply chain network centralized decision-making model; (2) the reverse supply chain network centralized decision-making model with carbon emission constraint; (3) the retailers' competition reverse supply chain network decentralized decision-making model; (4) the retailers' competition reverse supply chain network decentralized decision-making model with carbon emission constraint; (5) the retailers' competition reverse supply chain network decentralized decision-making model with carbon emission constraint and the government's reward-penalty mechanism. Building the participation-incentive contract under each model use the principal-agent theory, and solving the model use the Lagrange multiplier method. We can get the following conclusion: 1) when the government implements the reward-penalty mechanism for carbon emission and recycling simultaneously, the recycling rate as well as the buy-back price offered by the manufacturer are higher than those when the government conducts reward-penalty mechanism exclusively for carbon emission; 2) when the government implements carbon emission constraint, both retailers' selling prices of the new product are higher than those when no carbon emission constraint is forced; 3) there is no certain relationship between the two retailers' selling prices of the new product when the government implements the reward-penalty mechanism only for carbon emission and when it implements the mechanism for carbon emission as well as recycling.

A neutron detector based on EJ301 liquid scintillator has been employed at EAST to measure the neutron energy spectrum for D-D fusion plasma. The detector was carefully characterized in different quasi-monoenergetic neutron fields generated by a 4.5 MV Van de Graaff accelerator. In recent experimental campaigns, due to the low neutron yield at EAST, a new shielding device was designed and located as close as possible to the tokamak to enhance the count rate of the spectrometer. The fluence of neutrons and gamma-rays was measured with the liquid neutron spectrometer and was consistent with 3He proportional counter and NaI (Tl) gamma-ray spectrometer measurements. Plasma ion temperature values were deduced from the neutron spectrum in discharges with lower hybrid wave injection and ion cyclotron resonance heating. Scattered neutron spectra were simulated by the Monte Carlo transport Code, and they were well verified by the pulse height measurements at low energies., Comment: 19 pages,10 figures, 1 table